{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5hIbr52I7Z7U"
   },
   "source": [
    "Deep Learning\n",
    "=============\n",
    "\n",
    "Assignment 1\n",
    "------------\n",
    "\n",
    "The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.\n",
    "\n",
    "This notebook uses the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset to be used with python experiments. This dataset is designed to look like the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "apJbCsBHl-2A"
   },
   "outputs": [],
   "source": [
    "# These are all the modules we'll be using later. Make sure you can import them\n",
    "# before proceeding further.\n",
    "from __future__ import print_function\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import tarfile\n",
    "from IPython.display import display, Image\n",
    "from scipy import ndimage\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from six.moves.urllib.request import urlretrieve\n",
    "from six.moves import cPickle as pickle\n",
    "\n",
    "# Need to use matplotlib inline to produce plots inside jupyter notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jNWGtZaXn-5j"
   },
   "source": [
    "First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labelled examples. Given these sizes, it should be possible to train models quickly on any machine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Middle'>The following cell will not run without internet connection. Be sure to acquire the files in another way</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 186058,
     "status": "ok",
     "timestamp": 1444485672507,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "EYRJ4ICW6-da",
    "outputId": "0d0f85df-155f-4a89-8e7e-ee32df36ec8d"
   },
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "Failed to verify notMNIST_large.tar.gz. Can you get to it with a browser?",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-1fa9c6f44459>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mtrain_filename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_download\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'notMNIST_large.tar.gz'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m247336696\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0mtest_filename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_download\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'notMNIST_small.tar.gz'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8458043\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-2-1fa9c6f44459>\u001b[0m in \u001b[0;36mmaybe_download\u001b[0;34m(filename, expected_bytes, force)\u001b[0m\n\u001b[1;32m     28\u001b[0m         \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Found and verified'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Failed to verify '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'. Can you get to it with a browser?'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mException\u001b[0m: Failed to verify notMNIST_large.tar.gz. Can you get to it with a browser?"
     ]
    }
   ],
   "source": [
    "url = 'http://commondatastorage.googleapis.com/books1000/'\n",
    "last_percent_reported = None\n",
    "\n",
    "def download_progress_hook(count, blockSize, totalSize):\n",
    "    \"\"\"A hook to report the progress of a download. This is mostly intended for users with\n",
    "    slow internet connections. Reports every 1% change in download progress.\n",
    "    \"\"\"\n",
    "    global last_percent_reported\n",
    "    percent = int(count * blockSize * 100 / totalSize)\n",
    "\n",
    "    if last_percent_reported != percent:\n",
    "        if percent % 5 == 0:\n",
    "            sys.stdout.write(\"%s%%\" % percent)\n",
    "            sys.stdout.flush()\n",
    "        else:\n",
    "            sys.stdout.write(\".\")\n",
    "            sys.stdout.flush()\n",
    "        last_percent_reported = percent\n",
    "        \n",
    "def maybe_download(filename, expected_bytes, force=False):\n",
    "    \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
    "    if force or not os.path.exists(filename):\n",
    "        print('Attempting to download:', filename) \n",
    "        filename, _ = urlretrieve(url + filename, filename, reporthook=download_progress_hook)\n",
    "        print('\\nDownload Complete!')\n",
    "    statinfo = os.stat(filename)\n",
    "    if statinfo.st_size == expected_bytes:\n",
    "        print('Found and verified', filename)\n",
    "    else:\n",
    "        raise Exception('Failed to verify ' + filename + '. Can you get to it with a browser?')\n",
    "    return filename\n",
    "\n",
    "train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)\n",
    "test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cC3p0oEyF8QT"
   },
   "source": [
    "Extract the dataset from the compressed .tar.gz file.\n",
    "This should give you a set of directories, labelled A through J."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 186055,
     "status": "ok",
     "timestamp": 1444485672525,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "H8CBE-WZ8nmj",
    "outputId": "ef6c790c-2513-4b09-962e-27c79390c762"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large already present - Skipping extraction of notMNIST_large.tar.gz.\n",
      "['notMNIST_large/A', 'notMNIST_large/B', 'notMNIST_large/C', 'notMNIST_large/D', 'notMNIST_large/E', 'notMNIST_large/F', 'notMNIST_large/G', 'notMNIST_large/H', 'notMNIST_large/I', 'notMNIST_large/J']\n",
      "notMNIST_small already present - Skipping extraction of notMNIST_small.tar.gz.\n",
      "['notMNIST_small/A', 'notMNIST_small/B', 'notMNIST_small/C', 'notMNIST_small/D', 'notMNIST_small/E', 'notMNIST_small/F', 'notMNIST_small/G', 'notMNIST_small/H', 'notMNIST_small/I', 'notMNIST_small/J']\n"
     ]
    }
   ],
   "source": [
    "num_classes = 10\n",
    "np.random.seed(133)\n",
    "\n",
    "def maybe_extract(filename, force=False):\n",
    "    root = os.path.splitext(os.path.splitext(filename)[0])[0]  # remove .tar.gz\n",
    "    if os.path.isdir(root) and not force:\n",
    "    # You may override by setting force=True.\n",
    "        print('%s already present - Skipping extraction of %s.' % (root, filename))\n",
    "    else:\n",
    "        print('Extracting data for %s. This may take a while. Please wait.' % root)\n",
    "        tar = tarfile.open(filename)\n",
    "        sys.stdout.flush()\n",
    "        tar.extractall()\n",
    "        tar.close()\n",
    "    data_folders = [os.path.join(root, d) for d in sorted(os.listdir(root))\n",
    "        if os.path.isdir(os.path.join(root, d))]\n",
    "    if len(data_folders) != num_classes:\n",
    "        raise Exception(\n",
    "            'Expected %d folders, one per class. Found %d instead.' % (\n",
    "            num_classes, len(data_folders)))\n",
    "    print(data_folders)\n",
    "    return data_folders\n",
    "  \n",
    "train_folders = maybe_extract(train_filename)\n",
    "test_folders = maybe_extract(test_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4riXK3IoHgx6"
   },
   "source": [
    "---\n",
    "Problem 1\n",
    "---------\n",
    "\n",
    "Let's take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different font. Display a sample of the images that we just downloaded. Hint: you can use the package IPython.display.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import hashlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def disp_samples(data_folders, sample_size):\n",
    "    for folder in data_folders:\n",
    "        print(folder)\n",
    "        image_files = os.listdir(folder)\n",
    "        image_sample = random.sample(image_files, sample_size)\n",
    "        for image in image_sample:\n",
    "            image_file = os.path.join(folder, image)\n",
    "            i = Image(filename=image_file)\n",
    "            display(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/A\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABP0lEQVR4nK2QMUubURSGn3O+L02h\nIClIByFDCo61QvsDKmTv6FgEh87+CxebrYODgz9Cmk1wapdmKy4NcUoL1QYtifnufR2+xN7Yr06e\n4XLe85zDfc8xsvDozdvXzxs5RLOQQRwNep+Ofrkw2p81j8mfWRI12MZgS4pFkBR0stY8UJAUihDU\ngRc/dV12F2pD60xBkhSm2vRXy9MaAHLG2NU5AsCNDX+MMw9DZNhc1F2zTmCW3er4d+zfsPsgC9CS\n9w4U4AqJh8SqWCE+ayalPP1h7+nFTkNWBY2Vjyhhi4b4/cMSlsJId331A7ESit3+qNP3WAHlTGFy\nWblKuam8+ghJy/9hpaEHhcIWComOuVGUSspzsIyivHfwmn8d1c3d3S3/dooPv5DNJMfGy/etGsC4\nt//d0JN37SUDwvCwewMxRZFETZ4nawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/B\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAACIklEQVR4nHXQS0jUURTH8e+59z+m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\nTCR+/JxxIFacWx7xL/hy64LbSDDUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/C\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABm0lEQVR4nHWSzUtUURjGn/c9Z4xI\ncByQIqOFixJtNW4G1GYC3Rm1auFOECEIV/0J4sICFxG0iUQkyCEXbgLBjYs2Kn71IRhEiJBQbcpJ\n75x7nhb3zs2Be5/NWfze93m/jiCWoYe9WbpeaPt1uL5RU1MvX42RGEHxxTcyCEiGn8YMBoNXEVOD\n/nd1bk0U861dQ69PPZ/f5cuYiZ0K+GUodkHlmAEXIABg0PmeXGyDWiOi1uL2CasqCkDRuUfOi9hG\nczk8XrsAASC4/IFcgioSSa41MlW8IT8XcI4BonHBcTpXhkETlOjtOCLnYJGqRwzPbjWbJmrZJFcg\n6YkD3nEyw1UrAnwE02EJGn7Ngj0Q/ze9IrQASC4LXgLstSz4Bw7FjDH1O4AR+PTUBTrPO02DSrLn\nUfqQ2/n/VKwkV7i4y9BxJQ+1RtVYBVqeVRuRD+h8yP17UU+C9skfB8V42YpZOuc896bv9/UNP3z7\nm09yySEUM/Q+qJMk6bncfe7HiEF5tRYht/+0B2IaeQKIePQO3LiCnwcbOzUYJkP/A0AJoFA2a1sn\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/D\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABbklEQVR4nGWSO0tDQRCFz+4mIElh\nhAgWWglBEG20UgsLKy18lLYp9A8Ilrb+AWvtxcpHLSiWARPRgCAYRYwYLBKN3t05Fpubm9w71bDf\nnNndmaPODMLgX6v+VK7UAUPxJ/F4P9/KQ2sAwK+LwlrrhMKPvRwMAARdiYiQpFgnrM7D9EGS1pIk\nXcD2OnQPbH21HOmcr+L3InQEi7nC7ObxDz0NeJcLlUKuwkBh4ZHS0e72wA2kTSqF6YanjvdZ3R2B\nQJy16dsDCAAoTCxF0IfDkf+ispiJQ+Kt7KUak3EINF9Bn40koEIzTDLJtpBQqeNQEVkoX9ZO3jmQ\n9xD4TMLhqVBZjUOD5Ywo/6BSNL41GOg0hqp+9I7PY5FSAWAweFgQ7bte1Pq2okeLHR0dG+M9yy6d\nXlaaFM8k4DZMv01oXWiX/T6bOGtdx2GWwU7SYCLWCoU3czAqDkkKGycraWgFINU79KBVrz1cXb9A\nGQcA/xu5fc5cVd2GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/E\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAB1klEQVR4nHWSv2tTURTHv99zb96L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\nLN0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/F\n"
     ]
    },
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     "text": [
      "notMNIST_large/G\n"
     ]
    },
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      "notMNIST_large/H\n"
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      "notMNIST_large/I\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "notMNIST_large/J\n"
     ]
    },
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     "data": {
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   ],
   "source": [
    "disp_samples(train_folders, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/A\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
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    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/B\n"
     ]
    },
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    {
     "name": "stdout",
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     "text": [
      "notMNIST_small/C\n"
     ]
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     "text": [
      "notMNIST_small/D\n"
     ]
    },
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      "notMNIST_small/E\n"
     ]
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    },
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     "text": [
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    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAByUlEQVR4nGWSwWuTQRDFf7O7SS2k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\ngg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_samples(test_folders, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PBdkjESPK8tw"
   },
   "source": [
    "Now let's load the data in a more manageable format. Since, depending on your computer setup you might not be able to fit it all in memory, we'll load each class into a separate dataset, store them on disk and curate them independently. Later we'll merge them into a single dataset of manageable size.\n",
    "\n",
    "We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road. \n",
    "\n",
    "A few images might not be readable, we'll just skip them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 30
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 399874,
     "status": "ok",
     "timestamp": 1444485886378,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "h7q0XhG3MJdf",
    "outputId": "92c391bb-86ff-431d-9ada-315568a19e59"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/A.pickle already present - Skipping pickling.\n",
      "notMNIST_large/B.pickle already present - Skipping pickling.\n",
      "notMNIST_large/C.pickle already present - Skipping pickling.\n",
      "notMNIST_large/D.pickle already present - Skipping pickling.\n",
      "notMNIST_large/E.pickle already present - Skipping pickling.\n",
      "notMNIST_large/F.pickle already present - Skipping pickling.\n",
      "notMNIST_large/G.pickle already present - Skipping pickling.\n",
      "notMNIST_large/H.pickle already present - Skipping pickling.\n",
      "notMNIST_large/I.pickle already present - Skipping pickling.\n",
      "notMNIST_large/J.pickle already present - Skipping pickling.\n",
      "notMNIST_small/A.pickle already present - Skipping pickling.\n",
      "notMNIST_small/B.pickle already present - Skipping pickling.\n",
      "notMNIST_small/C.pickle already present - Skipping pickling.\n",
      "notMNIST_small/D.pickle already present - Skipping pickling.\n",
      "notMNIST_small/E.pickle already present - Skipping pickling.\n",
      "notMNIST_small/F.pickle already present - Skipping pickling.\n",
      "notMNIST_small/G.pickle already present - Skipping pickling.\n",
      "notMNIST_small/H.pickle already present - Skipping pickling.\n",
      "notMNIST_small/I.pickle already present - Skipping pickling.\n",
      "notMNIST_small/J.pickle already present - Skipping pickling.\n"
     ]
    }
   ],
   "source": [
    "image_size = 28  # Pixel width and height.\n",
    "pixel_depth = 255.0  # Number of levels per pixel.\n",
    "\n",
    "def load_letter(folder, min_num_images):\n",
    "    \"\"\"Load the data for a single letter label.\"\"\"\n",
    "    image_files = os.listdir(folder)\n",
    "    dataset = np.ndarray(shape=(len(image_files), image_size, image_size),\n",
    "                         dtype=np.float32)\n",
    "    print(folder)\n",
    "    num_images = 0\n",
    "    for image in image_files:\n",
    "        image_file = os.path.join(folder, image)\n",
    "        try:\n",
    "            image_data = (ndimage.imread(image_file).astype(float) - \n",
    "                    pixel_depth / 2) / pixel_depth\n",
    "            if image_data.shape != (image_size, image_size):\n",
    "                raise Exception('Unexpected image shape: %s' % str(image_data.shape))\n",
    "            dataset[num_images, :, :] = image_data\n",
    "            num_images = num_images + 1\n",
    "        except IOError as e:\n",
    "            print('Could not read:', image_file, ':', e, '- it\\'s ok, skipping.')\n",
    "    \n",
    "    dataset = dataset[0:num_images, :, :]\n",
    "    if num_images < min_num_images:\n",
    "        raise Exception('Many fewer images than expected: %d < %d' %\n",
    "                    (num_images, min_num_images))\n",
    "    \n",
    "    print('Full dataset tensor:', dataset.shape)\n",
    "    print('Mean:', np.mean(dataset))\n",
    "    print('Standard deviation:', np.std(dataset))\n",
    "    return dataset\n",
    "        \n",
    "def maybe_pickle(data_folders, min_num_images_per_class, force=False):\n",
    "    dataset_names = []\n",
    "    for folder in data_folders:\n",
    "        set_filename = folder + '.pickle'\n",
    "        dataset_names.append(set_filename)\n",
    "        if os.path.exists(set_filename) and not force:\n",
    "            # You may override by setting force=True.\n",
    "            print('%s already present - Skipping pickling.' % set_filename)\n",
    "        else:\n",
    "            print('Pickling %s.' % set_filename)\n",
    "            dataset = load_letter(folder, min_num_images_per_class)\n",
    "            try:\n",
    "                with open(set_filename, 'wb') as f:\n",
    "                    pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)\n",
    "            except Exception as e:\n",
    "                print('Unable to save data to', set_filename, ':', e)\n",
    "    return dataset_names\n",
    "\n",
    "train_datasets = maybe_pickle(train_folders, 45000)\n",
    "test_datasets = maybe_pickle(test_folders, 1800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align=\"left\"> Display Sample Image </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ipython_display_samples(folders):\n",
    "    print (folders)\n",
    "    for i in folders:\n",
    "        sample = np.random.choice(os.listdir(i), 1)[0]\n",
    "        display(Image(os.path.join(i, sample)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['notMNIST_large/A', 'notMNIST_large/B', 'notMNIST_large/C', 'notMNIST_large/D', 'notMNIST_large/E', 'notMNIST_large/F', 'notMNIST_large/G', 'notMNIST_large/H', 'notMNIST_large/I', 'notMNIST_large/J']\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABmUlEQVR4nG2SMW+OURTHf/c+t9VF\nKAnCRGhikQibiYGIkurbrZ9Awmo28ElUIphE4gsYBIvEZGCQlHagYqjX89zzMzzv2/K2Z7v3f87v\n/M/JgT4aBlbVzhUy/0di7xerWv06S+o/x0k2v54SQI4jF3eUZs7XUG1doZkQSfn1iLu2f8TdBuR4\nhECOQ5fSZGnm2E9DbWMXbuZJtD13yy9AKqWUmamFftTqImWyFD5Z//WbgByn7pgwfzhxtzZgWj/9\nPdnnF+6p6sPj1Z47oBmNUtPlbth1w/ry8xsqoDe3Xc4NDcPNuXTLTg3Xx34Lt+20+q5w+IfRc1MD\nGYJ5AHnVTa+9sALBoo42s2Fo9QbT6YpVDdcOkICGZTsNN46S08xHq9o5SA0ZuK4QvF9NkX8/RkAW\nBRL7vlm19X6ZKdPlTI3Y8luYt2pEnOsne26nVpdSKXLNyCTiwWaS7Elyv4dnAXv6bU9EuD4LXIiR\n1rVt27bt+OUgZa6m6Js1pZRSyuhwZEHK292oGq4e5Oyf3SQ1XM7zU7HzIgDCpb+87F/rYwYuuAAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABB0lEQVR4nH3Ru0oEQRAF0FvVM8YG\n4isyMDISs80FExMFA1PBWKP1C1T8AX9ARDBR0HizxUBFhRUDXyz4YEVwAgXd7e5rKDjVU+mhuql7\nwb8JrGGJve/itrE+CVGghIEk+bM/DlUYwxiyhZOpaKKo097AzqCJAJD7idUkwmEujYKxNBJvafQ4\nyBIUQ97aNJCMUMmv5jvGs6JZpo9rtQctb7Jzc986v/gUjWWML+3Lxl1XNNjBR75ujcDB+DOGyKH6\n2WxwBqpzQj96tBgSd6oGt32dTMj5/pWqVqarWhmuaqWowtMq3E0hu+5wz6oMpKCvuWzFJ6KqxcbM\n+7/KCODLfzy1m8fPKvEXrCqNz5Ow7xUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA1UlEQVR4nLWSv0oCUBSHf+fcAylh\nLjZIg05KL9Aq+Qo+RgTtQeBzNDY0BI3O0ivUFoKEm62B4D3n19Ki3tvmN56P85eDBHTuV8y+S+bi\n5kx9OLarCybdA/3rU3u47bxO+m0X7ED7XgoY3DRRQsVc4YF06BiwBCTAy7nF6PFleRoAmnNpjz8Y\nfK45e5SnN2Gl7GA4Oq84gMFPq0nP3JzUeioQtUTFP4c41m0DqK5pGgKpvAneyXWjXFTE7i6nX4NW\n+TUh6M14QNBfuipJth9reOyB5fznFyzieJh1vzYSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABcUlEQVR4nHWTMWsUURRGz71vJoXK\nZlItIkLSrKAY9geILjZibEWwtExhoQhqwMIy+AsUkbSSQrCVQKKNlRDQaFSshKSJccwq6yTzPotV\nspE3tz2cW3zfvVa2AECAsbPxZmVlPZrXANbVsSft6P1LZUY+cfLc+cOD1wtPByEKIPBQlcoWBhjF\n3A/p/QWCAT7GnCqVBcFDyIIx/Um17uMAGbf0+58JntPZUKUHOJBxYxRCzlXt7WqWkIA4L7Wr7cnh\n5v/GWMD2iptJKJZ3gnPlaApG//IWqX0xBTE+QqSXhPABGacazG0wjjeYfTDGG+Aw9QZ4BOBnEooC\nxGaDeQKLrDWYHTBepaDHydOYf3+eDv7seB159jkd/DWU9edx2C9yOHm83KvhznqAjNujZVvO1FdV\nmsfBx7i3f2AhOJ13quPdYdOBR6Oneej6lrTaIxhkXdozBLxbZgqtzpmZYrD8eLEKUWAH3kG/vq0u\nvVjT33f4A7d5mVhZacdLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAApUlEQVR4nJ1SOw5CMQxzSAcYQJyE\nizCxsnOIdz5GBiTEQRBiAhoz8ZoUtRLPUywrPydgA8bzYoYOumIKzEpIZSW6OsQ8iJT9Q0cmtyfc\ngJmrXk9FyQStHsiPVGUS9FQCu7zlG+bl4RgzNyXMum6bYGo/PUP7ehWHVDm0u4+c6YSpDhmjmJE9\n/ePYoSysW9aLgq07NuQajfegDE1vQX01HwyETH/qD5IYZCzrFG8ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABYklEQVR4nHWSvUpcURSFv33ONRrx\nBwXBVxAi2MwrCKZP2kC6PEKwMYUPkCptqjQWIUEbixRpFERiE0wnWAQnBBWdOH/37GVxZ8y9d8av\nOM1irXX2ZhsQJHu2vra6NDfDA6n18x0QmXj1rS1J8iGSXKcQaJzIlbq9XBWSW0wvPz5NGAFSzweh\n0wbI2PiapSjj+MuP3/96APjk4YIMWDxVrqTmC/v/GSaa6uV5zhv15bpsEGMMAzLeKyXt8FlJuTaZ\nLDmNqdefdt/Ocy53tVcJlLHiuZVcl3NYVcxCiJbNAHgHVUTlQC1tSMgMIBsruhelAkhnldT+xd6H\ntgk0jlzbxMc6YRY9dPYrSv/P/hYqOmVXa91yhN/eDSaSXH+f1FJjeRSrbShRElXbUEEYOoM9Kgof\nM1UmA8Jyp3U3Jrg4yeuri6Pno96Oe3GI+jVf+zLhwFT4uWmPxK7sNruS1PreGIm9BzW629duU9mw\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABFklEQVR4nKWSvy5EURDGf3PuXYVs\nIqLyp9iIaDf7AioRJV7DY3iAjUrJE+j3CYRSdESn0FhBQe458yl2z7XcG5H4yvnNzDkz8xEIu2fX\n40o/FTUsfeNkWwZyvhQMgHJz1HOZQUFD5VGv6oDs6e49x6waLMoAniUp6WIVqwsYKSpqWC4AeDh+\nKJVhES1nTTSGVLclkUiFZwh1IRAoCijLFiQu516Dd2+QJFXaaR2lGQLMANQOpUZl8K3T2SV+g0Z3\n/Q9vtrQVb/cOWFqbz0mNUSa7lRRa2+Yf/hvab7AV51GWZkxUHzu8TOoPl6OnrHxAO9+fGuz2o36g\nNlj/UTEmd2+4WoLBlVySxxlNMy14Z++gv9JtMQKfBwi1cVTN/eUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAACQ0lEQVR4nE2STUhUYRSGn/N9914d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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA00lEQVR4nO2TPWpCURCFz/w8G9HS\n9KIgWFho6QoshJQp3EFWmSJVBEGwEWwUREnAxkZ9c1I8jfeBC0jhaW7xzZ3TfCOt5RmiIkhDBpG9\nusAUIFNKqCKgUh3VXpr9bqX08zT/Wu0OnwAAsf6MSaYDvS4yd1MMDoyCBH/aUHOzW1OGzR2ucS3R\n4gkw7cxL8HGe8B9DQyKY4E8Cd9dUE/I4hoq5FxNiw8WdBfeTGgSA1N+yRrPX8VIZt7Pl5vsDbeYk\nI1I185xk8N2Ji0K0dA4KRrBy+QXB0H4U+5v+4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABqUlEQVR4nGVSwU5TURScc8pTFAjB\nwCsb4wKj/oHRxARISuISomu/QRPYaPQXIKyNblzJlp1NrCaugYWJhoaWB1FqxdTaxva+e8ZFKc/e\nzupOJmdO5p4B4rVStd2qFB/HEEAw86RYabUPS6sxsFSlJ0nP6iJEsHjOKwUc03kzM+9YjoConPEj\n3UEO5r0hhz0HuL2M72Jys0kjaY2NCYhgfKNhJI3NzUkRxvNzeat9/VAXAhDOzN+M9WS/WBOo4gy9\nl0Z9PqICqAgAGgEAwonbN2ZR2//0WzAIwcVnSS/Kt+ejCPGa3qfOpd74JtSihJ3Um/n0r9WGbFfq\nNJI0/ng4tJRXHy1dm5LT5N2raihixBPReK7TMmggCogLlyL3x0M1HZpceHB3eqr762B7K5Fg7vJL\n9uB5uhzkwFM6572ZT7v8OaeDchM5GEhS0OiGSV50erbGz3fw31JRgdmt+/dmr3TrX7ZLzUxTDU00\nmxROL1yPcVL++F0I1f4JAQjG1htndVkfQ5h+i9Y/1dvwY6JKVsmDaFATFI76ZU4Koa0gv1ZK2q3D\n96v5c+0fpUgXP71SwDkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train folders\n",
    "ipython_display_samples(train_folders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['notMNIST_small/A', 'notMNIST_small/B', 'notMNIST_small/C', 'notMNIST_small/D', 'notMNIST_small/E', 'notMNIST_small/F', 'notMNIST_small/G', 'notMNIST_small/H', 'notMNIST_small/I', 'notMNIST_small/J']\n"
     ]
    },
    {
     "data": {
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       "<IPython.core.display.Image object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAACFklEQVR4nF2RzYvOURiGr+c5Z7zy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\nb8iyTeXfXCePAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABsklEQVR4nH3Su2uTURjH8e/znDdB\nUElJQSzVeJsEsWDsIg5RqKLg4iYIXfwfnHRQECEugpNb7CY6OmiKFBcv4AWX6uKlqRcsFuutSd6c\n9+eQaGlJ/PFM58N5Duc5xwBwi4wcruzZWsy3lxqzM9MNnAwAgjNeW5CkLEqSFm8exLxrjNyIylL9\nTSeTbm8jGCSdytSWGHM2P/Ow8TVfKk/sJvXw5XQdAieaSlO9ObMRAyA59kRpM+oUzv5f6kRNDWEh\nCSGExAiXpGZtL6x/pjTqGpZ094ElgfP1MQzOqh1VN3dWYu6OO4W3imqVCayKewAmlXVUx+kTr5DB\nPawvlnHxrh+BlzD4PgA39F/vYud/uIAG42sEyQB8jGDzALyzHGB8UOO7SjU3vHYKHhzgiGKqC+RW\nG+COU1NL7aPkVsbrOSav78Qwis/V1tJxLAlmYCFxTi7rx6194Ox4pVaM1SGMbhWqMS7rQxlwRh+o\nk+rzlUOb8ljxwOWPaqd6NNo7PTn3U5mkhdmnLz5JyvT74rreG5uzvfr+37fNNH91F+6965lnKlQm\nxkrD+fa3uZfT9xcJmfgD847MpZY1ij0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABoUlEQVR4nHXSTUhUYRTG8f85545y\nc3IsaYaiEKpNteoDIQgjq0Vggn0xIAQV0ib6gCiiXauioGhlmzYRYUULF2YUrRo3Bn2Iq4lIhMQK\nrG56Vea+b4tBCLr32R1+8GyeIz3HyrffNdeK1yZfjv1iZamYfzb4B0uo58T4Vhh63Vq/iqc/V8+v\nwgCwgAcV2+j3kjMzE2h5PvSwvKTWeZQNyTZRACQgfHJo9BwBBHgXFUDxAoCvWXyn78DE/D1LFED4\nN4lVdO3h/i2JKimRgd7h0ccBaej8SKtc3dzl0tDLzI81r6aupNcq021+sH19KsLP5bzx+zMwChmT\nnRkYh8z4UgYu5JiPsjBQ1BYysDmmselrBjYtss5PZGBLLDvkRQbmv/nuqJKKzgrVUsfA7zQUv1qj\nM3YjdRWV9o+Nl+5/stRav+fp3emz6oL/yZKO6r5T22fVK6IqLLpwqSRIlh3h1u4P6lD1rmtSv091\nJ07NVKiF/Zs6eyrmAOTydeBk3JsDYEX57fjxhvrbysGLu27OWpK7kH8/8mWu0NCWfzQ8hzqAv33H\nhpMw5SO3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABTklEQVR4nJ2SsU5WQRSEv9k99xdI\nDPbGwpD8BQmdwYaHsOYhrHgDH8OCd/hrY2diKx2gsTRBAppAUO7ujsW9xnuJUniqPefMzMlkh7eu\n/msVrxL31L3LaK0hjV2bU2I9Tci/nx7QcfLIevJw7E57ADYfT7RfuNoufj3Kb75zcfEqSVnHCDAf\n3ElS9/09Bghjd6PCQgZsxWA1gAEGkFIDcFZH0MXU122tA/bbWR81zgWp7RxZYH36vHCNN6/y2kaD\n9HPCFFtbABfo+nrwOZWtDUpcQcoAni1zho41aO0u07q8CEp8oe7tN6EzILHjZrv3gTYWiweReWnb\n/jqTLf7RgOCm1Gydz5aJbDCkULYiBhPjVWwmgxBS/eNVgBgHYSrPsEDsqgfoeT5CD3dbero+fvZJ\nGfwuBdbHWG5PjiyZVdy0lv4ZsJQm8bwT1P8P9S8gDZ+0ltseqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABK0lEQVR4nH2SvU4CURCFv1kvLgQF\ndrHR2PkcPo6tnaUxsbCysbGz83VMfAkNySKoAY3sZY8F7LI/hFPec+ecOTMD5o6/lamCTKPYGQ75\nsWggmwDOuldx2CR7D8n9Hwy0Hb8dgmg49Vtk5SfDgSVBZICWJcYBZNO5banKsXDCQDZ6tEKxe9mR\ngRZIkpZ6KVXsf6xyz1xu42q+hTcgbwrveoAF7S5rj7XsK0avlrSQBVg8R0DrvLWXN1dU5ugk+Rpm\nQSNd2mwITOFNX6b2AZvIm4YOf2oNlWU19qn3vilrzuSck5U+F6T38JnVJyQMjJNrU9ivUNLulY1L\nyy6Pi2w6JzqbpLXDlKQsfTsd7DwwZ/42vgirEZDNn5LVW+urefHvAAHmjqplAASxM/4BfSsNgaPH\nS9sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAACUUlEQVR4nDWSS0iUYRiFn+/7Zkal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\nJaH/ZMsggpZQUr4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAB5ElEQVR4nDWRX05TQRSHvzl3ANEI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\npFgcF48x/gO5+ufJtpLnMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABbklEQVR4nG2RP0uVYRiHf/f9nFcS\nz9EGCRrMwKWWSFpK0CH6Bi4aNCpELS19gaAv0QeIoEGIoqHIP+ii4BDUGDRZCDqkHjjPe18Ndeqc\n9/HeHi6u33P/MUmSYc+WTsYuddSvXz/O+Np/mKsan350ChB8fzAz0VKzXlED+Y5Mkg0SU7VPEGyl\n5GbDmulWBjIP/0o+AF3LCZHyphqaZKZ9anq8GS1N5+6MTOjjWYsC6l4nW1SHO6JI1fg6mR4f5KaG\n6bo5jyvpk5wmRHOeDTt+rbpM7exSE2yXc8h0g4Dg8X840O2iJFm8LTzJ9Jkg8+5CaTq3r0oKrXUT\nTZi02A6L6mjvvNDWFpkeazbQrPfFhVm5TNtUNKE0N9azSAfvz9vAxAaZmnWl4kvXLBHAk6Hr982n\nZKA7VezO3KovBMFGGoJuEmL1uiT0rW7eObWvrBwRAIf3p9rVv1vLXl6rRyYvn2Zh5iPp53H34ovn\n9sf/DbSBqh0k7mwtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAzklEQVR4nO3RIU4DURSF4f/cmXSa\nClQFAsMaAIeuYUtIkm6AlaBBkFSgSDUeCclgypS+dxAzCW3yOivgqpd8Ofee5EFhxMXqs329KRnB\ns1P2exHFm5NTjqIO+RFEYzia/EeAWgHZR5AEUlnr6dVZ+/JxRB+883p+eLv/bBvntPUlVQnrHKWV\n6m9G/67wH8SPh7YAFSnt5fJQobYIlvdtt9mRCWIymzWLcwIsZzSc2BuDiXgKJA462ZZEPOr09vqk\nmU4aqV9gbHfd9/ZrdfcLWTVSGyxjs0UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test folders\n",
    "ipython_display_samples(test_folders)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vUdbskYE2d87"
   },
   "source": [
    "---\n",
    "Problem 2\n",
    "---------\n",
    "\n",
    "Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint: you can use matplotlib.pyplot.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def matplotlib_pyplot_samples(pkls):\n",
    "    \n",
    "    for idx, pkl in enumerate(pkls):\n",
    "        with open(pkl, 'r') as f:\n",
    "            dataset = pickle.load(f)\n",
    "    \n",
    "        sample = np.random.choice(len(dataset), 5)\n",
    "        plt.figure(idx+1)\n",
    "        \n",
    "        for i, k in enumerate(sample):\n",
    "            plt.subplot(1,5,i+1)\n",
    "            plt.imshow(dataset[k])       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DR4DZgTP8+7/9LV5/9xPEUlVrLS7RyCXlOS4rz7BM/dpdrCVizWFkQHwU5t+E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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107446d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Sie23xMvyoZATXAPfWgelB4soroxTOPQiRT1prmxdxcCBr5AYiRP+U41rp/3kRNFFTg0N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\n1ekxapPvUbcuQm1VmMlIDR1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kp+nfXsv0zn9JUeFP+LhjnPqeOOZZUOMGPlkQ3VnMW5sP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\nexAF4V1AAQUUcA+iILwLKKCAAu5B/H9aN/xqVgGX6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1131acad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ysQm3O8AOcRV7NkAoCsnMUkvr7XSvPOCRoE2CQnYrrwf3cS7aRnih7IhaKyc1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\n7xJKKKGEtyBKwruEEkoo4S2I/wd2JaY2n6sHewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113365090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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J85QP3TUVp/ZyNzWRWtyp1mKJi+HEMiFlYqQgHPERxkfxaeQDuFGx/VVAI52+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\nkHC5Sbk89N+1ixM/9RNMvhwn893TcKU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45iE7gQ+4Gf3QfsYf2cssboL4aCBBA3m6iZC9\n2s8FZ5GWK1Hm+q0Mpt1kx7LMLi0RVkGKQEs9pFvg0d/x0f/YBXpbf4u3/zVM8WqUJYsNrBchVwRF\nJ3oMuI7I0fK0VtM3tsRJByJLhX5vhWqCiGb+0Ab/34qcqsn1aJ8DwDELtHSB8yMweGoXb3d+jN7Z\nh4l9/V3q3ttP5oUImdcS5JZkyhk99L96zMoU8C61cLL/z/6Spyb+kT/yRpi+muLKIGTzq2e6ekeZ\nYm1b0WEMAhxMg3oTpLkE1t6LnHS8i5KyUkpZV3qkdUgh+F9JsMv05dMoKcikyvMh/boOxGC36ILz\nT4H9iXquXf8Irw58itLgMKVLI2QjVkoFfZPPTa2W1wy8fBXYWv8Rw/sJaGyDU4dp7lT4/cXv89SN\nXzAaXGJKq+MtxCkGRmd04a0liuNJTjbU8cldVhwH4KqtwNkTNvHoogirX0iCECyG7AwvuJhbspG+\nCDOXYxQacoyXUpTCCnZgN9DphqYPQd0nHPSHn+KVS8e5Nu/G/eYQEcVFoXRFmBaL+pPQZ5b6k+ur\nkZMeqrcAIy4SfqyTgWfPsT/QQBc3aQ2HCczEcYyVYAgKNyE9A6/mxb2FEQeupCm3bqPiNvYfo6u7\nhBgu/ECsrZW+Y0eZ+71Pwv+Mobw2R2LJQ9muoPvo9M86L8Z7emPDO6tle/wfbvbAUO9LL9LTeIzm\n9CSu2Vks80uUbkFxdLXddRLR+CphdMZI2SKWmQS3bGB1QV2DE8+sg8YGlQIpJCJ4SFNPijbmufjS\nNN1PWNgjx2nZpeDxwqTiZ9LaQyzRRMlWj1eBG5EblJYOMXa7lbmUi8xiA9mUiniE+qYTXXn3IWYN\nrcC3jVXd9CGqcwcdPHq4DqlqzEgZvSMyTxy2bShTKaciaQOfFYU6cjjJ4SSFlxsjy9B2iELYSXJZ\nJRsr0phNYw8nmJjZza1cI7OLLuKhMZTCcQjqzcQ43zA+GRCKuzZOekLvcPvGCNL+MMUoFJOiMzhZ\nHbxmQajDc6xeviqsHk5U4GYJulNAqogtHKZZi/g2ztitURhChEA6Eak5271gaQJaIOX1sGBvJZoJ\nsDzt50p0kvZEANuMhfFwE4txizgK/bYxBFVFqFNpDS/aVvBN95+93dc5YrVRlENE80n8YRl/sB/L\nwk2IG+4VYeoyLr7VSJ5SJC8yI3aCJQrX5uFckxC0xMX9OxPgyWjZBLywxwrZJvhBzEL93maidfsI\n72klF/cTnoX5nIIvEsE6lWcs2ky87xpxy0ch0ghqnnKfqQwkmNK+r42T6sO3cS4cBV5nIfY0/bd8\nLEc7CFEksOzDP5/CHpYhYUHO2ckW63lBmuec+zBRZx1pF2Q8KtbmArZAng6CdDKHI1ygb0RmtwVy\nMTE+5RHnTvoCoNgCzOcOcqv+JBNDIeJHHBRHvSjjPlYPYkZjy0b3Ux21bI/fdPxl18V+uiZvUVLS\nTGQSzKUhE7vz7yqhu8WsiGOVRqdhYqlAZihI1rtMHWMEcGCliBUFmSz52RS5OYmSvUTTMTj2cZB3\ntTPsuMDYxEkWXu4mMyCRmPknZl45ReiqStI2SjFoXOY5KEfwlhCRC39XUbuvbukQVf8H6zn4rBsH\neUp3CNh96ztJjj7rvWOZupxouhIlLORxksXJEs2EaSbGPiLfGaL/A58jdq2ZuvcUPIMZbLOzWF4e\nJ325wKI9STI/grIUAW5TnlMY/QqV9rnaOdn78+8xGI3QH4S6mOjaekYJ/YgJ3dF7GThAOQ4/r72M\nn/WtU8bcd7DW9KKn/e5AXNNfDw0dYDsOnIXph5qI+h5lZuE0t392iNGXf8K33jqBdGWKYDILycuQ\nzVH22hiHj64KXr6qbwXfdP85eybI522/ItMPN6bchHIqsdwcvXlIymtjqYzQ+8vCMsSzYJmH6Rxc\nXBLVs8ngKEBzAXYXwOeAngB090DpBLw6bKPtz/ex4DrJ+Pw5bo4fwfUSOHqz2N7sQxoaISjLELwN\n0gSoepy/vvrQ+4yR8T01c7I+jOszgDlgjoWrU6RmZYZsflw4cMgydbkiliyQrkPN+lFy3cxaXuNW\n4+eRW90UW0HtUvA8Esd3Oko7r3KQVwj0xnn3n2UesUE4C+GMCEoPNMHRkzDn7eZXkU/z69QJYre+\nSfG7Q5Tmi5R1iHGqURvuyw5LbzBMfTBMkrJvuRYnnQSUVFhOgJIowXwaSGNF2CR16BQVi6B2gMsl\n7HTeA/UUXbtZdh9lduAQkREJ8gFCt9sR9qgoq2Nh9ZrWTrgOR5eNpscdOFG1WXJ1NtwvZgk8fue8\nprqcrrwVrGRwY8NNiiastJGnh9yLYQrHThFe2IU8o4A1AbEGSMmIpigjwupyaCd1Ut5XaJzZ3Fv4\nbw0iA+GYiHRpoJwEy4l4troDyYVYnmYpn9KiW1b10D19ONQVGJSXwEYlpzuO6xEW2BabCLZwtAOH\nIHXMiSXQRmpqP7NXHiYivU0keBBiGe1qQUTntK9Teu1ob03xmCNB0pojHwclJganzewy1o0W6TzE\n8kBcPNE5LfDFhuBSQpjQfI3Q4ARXM9ADniUrnjM+SvVdLE8eY6b0mCC+mILZDMzGtfs19m7d3WeM\nZLlfMLbLFJAiHUqQDkH5NCkd+sqgFTgIdcMsOh8FrxeawdJZxHc0gvx4CJVhmnDSmkzj9UKrDYoW\nYSO3AW4ntLZAzO8lzGGmM4cgLkEwgmDYxuqJT+39R1K3+TygdRKsv2+hqurG9g8NJidrYXKyPn5T\neDE5WR8b8bLtytuECRMmTNx73K/wYhMmTJgwcQ9hKm8TJkyY2IlQVXXbXohtdiOIwOgv3UF2ChFv\n1gdcNXz/XSAEDBi+awR+idgp8zLg20D2K4jDb65rr48iQgB+BQwiYpa+WKXcIxVyf1GtzO3gpRon\nW+HF5OT9z8m96D/bwIlvHdmaebmfnDzoOmU7FbcFEXrajXC19gOHN5C/BTSu8/15RD4VI3lfB/5W\ne/8l4GsbyH4FkRXRWGY7cEp7X68Re3idcr9RRW5NmdvBSzVOtsKLycn7n5N70X+2gZOvbSB7V7zc\nb042kH0g2sp2mk3OAuOqqk6rqioD/wF8YgP5yrQVAKiqegkRt2bEJ4Dvae+/BzyzgaxetrHMBVVV\n+7X3KWAYMXJWlvvhdeRqzX62FV7W5USrz6Z4MTn5jeBEv85d959t4OSZKrK18GLqFAO2U3l3wspZ\nrSCWBJ1VZEEEPv6vJEm9kiQ9e4eyW1VVDYEgDRGouRG+IElSvyRJ/yJJks/4D0OmxLeBtmrlVmRU\n3LDMO2ArvGyFE9gaLyYna7FTOYHt6z81c1IhWwsvDwon8AC0lQfJYfmkqqpngN8FnpMk6fwWfrtR\nvOO3gL2qqp5C7GtYSfRemSlxnXLUKnJVy7zHqIUTqM6Lycla7GROYHv6T82cVJHdCW3lgdcp26m8\n5xD7YHV0ad+tC1VV57W/YeAniCVSNYQkSWoDkcyG8rFJ65UbVjUDEyJT2WPa79ZkSlyv3GoZFdcr\nc5PYNC9b5GTd+lcp1+Rkbbk7lhPtWve8/9TKSTXZGnj5f+dEL+9BaCvbqbx7gf2SJHVLkmQHPg08\nv56gJElubSRCkiQP8BFWZxSrTP7xPPAn2vs/RuRmXVdWI0yHMVPZmkyJVcpdN6NilTI3g03xsglO\nYPO8mJysrv8auZ3KiXade9V/7jUn68rWwIupU4xQ78ITvtkXIoRmFBgHvryB3EMIz7Geru/Lhv/9\nEJE8Io/IkvSniPCbV7Syfwn4N5D9N2BAK/+niIOUn0QkGtCvqYf7NFWU+3QVuTVl3mteNuJkK7yY\nnLy/OblX/WcbOPFvIHvXvNxPTh50nWJujzdhwoSJHYgHyWFpwoQJEyY2CVN5mzBhwsQOhKm8TZgw\nYWIHwlTeJkyYMLEDYSpvEyZMmNiBMJW3CRMmTOxAmMrbhAkTJnYgTOVtwoQJEzsQ/weLV8+coru9\nyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105f9a290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OHLK3IDtTWVL6rg4CQRfsyxEShQ7c4SgUdKfTgtJ/RTV0J9VdMADFKSheY/iYxQuff5gW\nAZGXxiCT91QaoWIUK1EnXfSAkqIYyzD+WCvvfOYIiQmnJLy1Pb9kMzX9J7Tw1iffbeGtGXQemFJd\nLr5q4TVoUvA+le7q2ibpxRbssR6yiU0Q3jMoX8Uy4Q2r66X+Qd2EJRfn78bJnc+T+BftzP3cUzTn\nbJpeGsSSinPqNA3UxScZYtibILx/0MHRy6JxiigfYiU2QaZUw/+ebeg04ESAfDrA/KKgaWWerQgR\ndFEm+p1A+7CNOZxBiX0tvPUQ4Bfa1fXQ4Ra6Z6uQZEva7Ai53NsKyaYFmgpbL7xrObM3AWUtx2wr\nErwjSXghhdlkV+hA+sE6umsn0IXknYUFxOAQLM5DyXGWpaynr8bG/tfloCk4Nb6Lt88MsG94kn2F\nIHkH4jlISXCWqzR10kULBMg5Ed5ZOkFuKkRPKk83V5TqgY8VdJUqThQpOXffdd2qOsJZ6yma1SvQ\nME1KryvP2mrsuuB4N9f0Ww1+Aa5gp0zGvusg4jZP5m0e+1mYuQBTF6DgxetbJlghMB0Q+t0tv+mq\nSBGj6HXV6rDEehEBoigjUYCyClNtka0OK9CH6ztX65p6jmUoomIRK7HlMiXSnablvnG6FqYIvZMr\n6UQlM5fvKi1mde9q3QP9A9A5M4c18g7kTVbW26uhqW5TtqDDeGIP3x/bwXzHQQZ/6hA3drjMPd9W\nuda0BjYqvCXwXSGEA/yhlPKPNng/qv3pVnuRyJEk4ak0hie8tZ6pK6AjEPoEHEHSs7iIWBwCd145\n8nApC2+/9Wol1BLeO5l5437M4QvsLgQpOJBwyr7LKtRJF1E6sk6Yd+J3MzS5n4dTF+mmbCErMZM2\n25cUbC28qf7jXYbP1FQRbFWBhmkCqGZp+brp0MtcbJab71aqW3nqU0ybjD3nkHgtxzOfsnn8C/Dq\nn8OtK1AsenqWCYEQWDaI5X28LpqkaMLGWiZIG0EEZYFsQmmPLmXxoXtIdc/wn3cbPPwDw4o9rrbm\nveUyJdqdpvfeCbqnpgm15krzV4GiiVt1ld9S2doP/Y9B16U5rKl3IN9KyT+3JjQ1tH1cUWYs2c/k\n7YcZPJri3E+eIN00xvyF8S0X3o9KKSeFEN0ogl+RUp5aXuxF3/cBVvfrVmo4zSLJHmuE3dYYUZEt\ndR+TMiMLQAQgNABNeySh4QSMTID0HAPAGjpAFfxEVq/NHS3gnkrR2mFz8HMh3r65nz8ZaWIqYXJ7\n4dtKwDRElxcoj/f9kD+OfSVAMRCmecRiN2qGn2EtsbKWvfbdgH4TQ8AIK9C5TprobrQH5LHNbVbN\natXLF1X+EO9StyDJxAO8duFh/u/IPbRceZPDzpssUmQWuGDDqQTccicZd5Z1jbr6z9AfP0vqxhjX\nUVE2B+qordYYtfqROdHH7AO7GLrZzuKZVvLpJmwRw8UsDwq+sVcKkIb6dA3vO97vqv+l4ZVhld8C\nhBfvPp8bYy43TsyAP3IB3miYJo3JFK31qOFkhzHJY4Fb9AVu4gq1rGmlOP3q0MzFnW0MPdDBTLaL\n4mtaSdFeDj1MrfV2/DM+cG/O4j57ifm/PkN89nWc2Sz5oewa99h4nPek9zkrhPg6KitYDUI/2cBd\nK51DLSTYT55djBIlUxLe3rqU0hUiCIEjEHkErBeScGvCZ1podBamhZFP7x3NwtQCnZ/Lc/QfB8nZ\nj/Dy0C8w/rIg9+wFGPlW+eq66PIUZbtXUU0M3gFjGtrHFCuOoUzIPyh9un5ohu2ncnXzS+USddHk\ngygd0UQRZAuquJI5clX4fSH+9a+KN4rFAC+dfZA3r9zDF3J/wBfsi4yKIgsSjgh4LAQv2zsYsh9j\nktPlKtXZf3p+/tOcnFpg7+AogTqbEECFq+fw4hse2EXilx7m8rcOcu7aAKlMH9LqBUIlDUjqplWb\nZk3PreO3VxpeeX8Z/7WeO0dWk02rsgGwHJjKAH+3s2GaNCZTKvvyLsZ5iku0McZVllhiuZDWqD4/\nt6OTy/cfYvx2H8WQ9jeFqJxvrAU9S/be4vVJuD1P3pLY5r3IvIPMOihlZmWsW3gLIaKAIaVMCSFi\nwIeBf7Pe+5Xhn2hJWolzgBQ9jJIlU34+ZfejAFxLkO0PsXhfjNzVAIgiioUt330b6bV+yx5QWITC\nTUauOzz/wn5uyA5GpxIkhl3IlAVN/XSpkiaOAwtJjNwMbdkMeyKQsFnbFPs+wNbxSoNYtxbvf1c2\nZeGtuE9ikMxkSGamOdu7j86Dn8XpmSTZM8vNSIB0sJnzwztYfKtZOU1pjCa2tJANRkZXGZ9wYkHy\nvc2kWwMsmnmSMgVuELDAdUDI5VFtWm33C259+AV1tdVMnxcr/PZPC3ympK3hE60d61wSUSITgu6X\nFmlZWiS4UFxmx/ej2uwz4g5w0Xmay64gR3oDddLSS0LegXwOicBuwKOxEc27F/i6l+HLAv5MSvnc\nBu7noRTVDOBp3sN0cptR0j7xXenZkJYguTPG9N0dpJ6PgtBcEvTu16hDr/pVzgNJ3nnL4fbwUbJE\niBcuQUZCqkJLbIAuvpgEx4HkIkbWoDWUYHcT3M54Y9D7H1vEK+8m/BZcP7T0GgUmObN7P4P3/xL7\n77vGgfvOM97cxItiN2PPOcyOLZSENw3QRKdx2AhUUjaBJAGkQE6BE/V8Ql7f8E9lV/us5796y1Z2\nyU3mE3/UvY2SBU1YIyGi37CJ5ApYU7L0VrWrXcPvXtTnh3KHOLf4ERbTk2Scc5TDWxvRDGoZfv0j\noF4StDo2kttkGDi53utrQ4862tYsiWay7JiaoW12jpl8gTRlUvl5wRYWU+Fe0q0HmQl1IyuG+XXN\nlankQjUAJOIGiXi7d87vBFWony7VvngHnHmQaQJNCSKtEHBA6PVF72NsDa9sJapimSIRaO+irbnI\nkeBVWp04V6cOcWthF8qwlUDH+S6lsyzNODjDQZxwK5lIhBmCLA3nyGbWwyfgOkbDmne1JT9Gmjam\naaWAqdcpS63QFFT9t9x9UsuqXH7Y5vOJX8N1IdAMwX4y+SmmBgNkC5JculKE+puur7RRgjIM2Ddj\nzHynj+yFDGS1bKkIAWsA/jdUK55ndbwHVlhqVIfoqYZFEnm6hhZpXowTzKi4jlrjnI3FbfaQ414m\nkN7/lfbzxohbPQ/UBNWjpl+or5fb/eO8DcyreLJIHFpR3sr1xIVtY4MwKEdrF6G5Fe64k769ST7R\n+goHc9f58umT3Fo4CVxCBY57a+rHpmApzuzFNOmmNI6RI08ae8nFnlp71VwtuI6pPH8NoFoEtLFE\nD8MMU8RiJ5U2jXLM8dai2oK83tiZRp7nGzBCHdB6B4vOJFeXInTlIVUs18QvV3Qt/fHdrUDsAhiT\nqDE7pa+0qS2VVoLfNuVvv58umyC8hRB/DPw4MC2lvNs71w58FZWgYwT4lJRylVD3eqCJrBukvBrm\nvEXkQpFwqoCZKDdbT3NKlkhpMZ7bzVjiHqbzk0imvPtuRPL5iak/9fGXwFXUa/3vVel100W3vQBk\nIVhQMV3rXQ+9bqwkIGrNdzX8NPkbvIzEwH+nrtgSXlkH/DPTUjP8izf8MCsKtwcL7G+7xT1t0+x3\nR2jOLhJ0w2D2gTvimR68h6QykEqSRaJio74GXEO90PXRxHUNpNu42cTP+SHytLFElA6Miqm6Lrna\n/TdmslmOvwSuoGjyP6onbJlM0StoBeZOg8CJIM58gNnzApEoezCqxaWmTilEsAX2tkBHMoFxfVSF\nIVcI7dXkjG8oEEBXO3R2YLUWsFry2LfT2MMplUm1gUG0npJfBn6k6tw/B/5eSnkHyiX6L+p+Yt0I\nAe0w0wxnLDgPLKl/tKFCJwNyAdu1mEzu4tLUSaZTOzxS+ke49Qhxv5PK9n0vACdQKdnBN3qugy7a\ntONp84JyqEBgtes2G9WeqOqwA2/HC0K+QzuENRvdA3yu+sbvAq/UCb/jDChHCviPCOVlLAYQpZ85\nPm18jU8m/wT37Wu89YpgesqCQBAMvbrWr0XqyOEQ8DDwT6gyGTREE8cxN2zzNnC9jUXcirNrH/6w\nk40eAtVzTwKfp2wiBbaETypnFcE78jR/epHQ0wmKnTZFKjMr+c2xFXN2A7r74OgJ6Nk1iWmeRY0v\na9ulywNIUD3NMuFQP3z4EYI/cy/Nv3KMyIf7MJp1Patn9qu3blV4MZbVS1U/BnzF+/4V4ON1tKIO\n+Bm8CdhJPtHF4nCA+G0oZGo7wwVgOAbpyVamL+0kPdPc4DSzWi0zUUmXdkF4F7TugnA3auKkp9O7\nKSdRKmGddKmaTuq+v1LsUs1613uIqmPFm69Qz+rf/vv0o0adCmwRr2wGqtvjl+4uoXZJx50Guw4l\nGXAu0jP2FpmhOSZuQTptqI5oVJsA/JqYQOU1jFQ9pzGauE7ZYVmPnlerdUUCpImSJ+iznze69GYz\nDgfVd/QAWRpMtoBPKvmzo3ueIycus+fgCFY0i0PlG6+2d5dEqAHW3iChR2NY+/Jg3UZpknrwrzVQ\nmb6j3O8MIelvTfBw/zhHT2TofSJE06EARtBvwqpPeK/X5t0jpZwGkFJOCSF61rpgbfidC6CE5wES\n9hg3M2E6JGh/T7U+HQDCBbBuoGg5TAOKtl/DEChNIAAMgHEI2gT0CrUX2/QcuDOokIGaYSBbQBcf\n/CpB6YR/vWk99sNqO6Nf6Kx0vdaYqgV+XaJka2nSCPzuFKBy+WaRcndQakHrQcmBf2TQYxUZ/a5L\n6h1YXPKo7Q+Tq/kQfX+oEZHQEE2U8Fbf62XrautyiiYm2ckS7TilJGY6VUQ9KSM2AysF5AFbwieV\nlv9+RnmaizQzSMZLwlOrZbpXWXhzbkMQP9jM7R/pYGkuhvuK7TkrdR5/PSuvtUZVt1eVCcoCP5R/\niU8mvsrZ3N2c5QQOcRZwKa8fF9STD2KzHJab4FLzOwaBaBSaekkVOxhLBijYlRkE9GtRE1uI2JLQ\n7TzkkzBZ8Gq0lufWPzKXiWZZks5um/auPNmAIBswMHa4mF0GqTlBchZcG+qZuDRMhrWwjNv8Er2e\nx1XbN6uv8a260FqEIcA01MAYBqupiNVUJEyOCDky8xbJ2QCuXVdz3zX3q7+VpuXQ0hOnu2+WtCHI\nZHrAtNT6dRyQDkgLZACrKU+wLUn30QwHDiToWZplcSlHcrwsuMUyE4yGX3j7hfiqg9yqNJG2clj6\n79AoEYUrMYsuRjQMu7sgEPFmDl59hYsQEr23r/os7+WqD7zf+M5Xn1texsVAUoiHyM5HcHM2uMvX\nxVc3u8EmVreYsoFVmcB607PcN3EJY26Ca8X0mlHaJS+cEIx37SB1+Di3ezpxSnno9MxbH9V9UPvw\nbITlEuoWtPcYHAze4MHbLzE3GORi5zHEFGDr62vNiGtjvcJ7WgjRK6WcFkL04Y9erYkXfd8HvKMa\nFVYm2AXcBYV5SFyE6EJ5LPK7NgNAJxBxXFoX5yE3DJlFlKpSGTO+HH5tU0+iINaU5qkffoEnnvw6\nQ89Lhp53iXywncjTHVx+2eDtv4XM4m2UwzLtb18DdPHT5ABwcOWi1dWV/hM6TKneOPaVhIi21URQ\nSS+9w2iCUAhiQdgJYrdL7K4FWu9aZECMsJcZbjzXwoW/bSezeA14G0WTl/SNG6DJC1Qsj+dYHe1Z\nGX4+CbdkOfTEVZofeY2rz/VwI/c4RAREPYXBlWobJtsgduc8PY+Ns1deYvfz5+m4Nkh+eKEkghvz\noIywMT6B+F98hQtDg4yjsqfvr+OpmvM1R/QVZjiWtBnbuZPQMyfA7YJWCQEXIRwM08G0HAzTVYfQ\nh9r91cLBxEbvAGsgMbE9O7oS0GqHWGVXV1udqO8BigQpMP36Dka+fYDs6EuQf4P18wmsLlP8imAe\nNcqGiY049H53AXssTmDJLvUeqHyffsmhZvgmF3Mnub30GUayM+TlGJV7A3ihljVdnuptWE2SHT8c\npv+pMKnvBzn1fXhzrId3Xr+TuWvj2KkF1LbMo1BRs5VRr/CuHg7+FvhZ4LeAL6DCDFbBk3U8wm+X\nBWOHg/VQDnGrQG5Ykl1YJt5L+nI70IFLUyoLqSXKLwxWn6r57UwhhGVgtho0781w54kLfPjB1zn/\npkNsyiEH6o2KAAAgAElEQVTcf5LwJ0+ymGnj0nebYPEA0IfK5/E0HhM2QBdNE+3QWBvNZordgTFa\nzILydhd1iBpUpbBaGdWqW+nTBBlUQswNq09p4YowthXFCYdx2kHudOg8lqbzEcFdZoZ7jVnEUJBr\noQgZjqMiTYZQS91fhIZo8kG2anl8MJhj9+5rdB2Lkb74CDPR/So5czNl9vA84D17TPbc67DrxgJd\nr14jdm6o1JkbsTkrDKBMgMOod94on0DrJ36Bu2emOXTrZomrq2201ajOM9m0uETvzSV6WKB9IEQm\nGoNuBxFyEYaLaTlYARvTcjBNJcRN4WAKGwvHc02rTCiq10jvnBLSZmmLb2fZ7xA5wuQwchEmrh4k\nm2uG6SIUrqH6zosN02R1maL7tVZsIkAr5kSQ0CtpjEQGI8mKwpuq8xKDWbuHK7nj5KIG0T3T0Oog\nqoS34ZuJqMFNYLouZq5AuMWl/0gzex60MM7Z3B6B0VtRbpnd5ItxLzvhAEqJ09z24qoUqCdU8M89\nSnUKIUaB3wB+E/hLIcR/C9wCPrXWfdZGpZUu1pmi7c4JOow5AtHCsmhUTWwDiJjQYpqE7E5w96GE\n9wLll1dOSlOJAErYNAOtWB0h2p/J0H1/mMRUhEv/zmH2nGL/aXqJczdjuBRJA/8FpVWlgX+nb7gF\ndCkPVCej53m09z9iNjsQmASRQ2UJ0hSpQ6RUT0i088AWaoPUXBAyYWQmgsxFyBdDzNsB5pdMEkOQ\nWpSEJ9KEvp/heGyOe5rmuPmmSSB9EOVnuoYKUN9amtTTzIqOuZTH/fYI5tUM+64OE5l9QcXq+n3O\nngUh+kqaluk4rQvjFMfnSVOelzUelfxXKD7JAr+rTzZGkxqv1m8oq/XWqzXv8fPgfglSvM1d2f/A\nvkAMIi7CkirISbgYpsQwyuYTIbQG7ZYOvwlF/XZLQqt8Tn03vd9KoNuEgybGifvh0q+C/X1UoPT/\nsT6arIoqE6zoBXGExfgE14dCWHnI5ippV0vz1gbGMA73hs9yZ/uXmPyhAOPdBhQkFjkELhKJiUOQ\nAgGKmDhEyNFKgtZUkuZrSUJjOeYvBEneNDgyOsqd/XBzaRorfpF8cQG1YKqxed2awltK+dkV/vpQ\nXU9oCGXh3dSeYOehUbqS0wTD+Qrh7deChFBmS8sURNwAzW4MgwBC25qwERRA+JM9+iCbkASRxAg2\nR+m736D3gwkyv2ty/ctOKRhoxu5mMHecieICRYaBT6MiK1xUx/xXeAnkN50umhUPB6/xQMs12vqA\nDsrb7DQiUTTxdJyl4/tdQPWnJaWIu0DagdECjGVhJgXzt8G5oP471AXHumBHvB8r6wK/gEojoFNe\nbh1N6kEFWZIFeHkM6+Ux9nK2ciPc6uumwH2r7L7W5rp6vQqV+Kmq3/+mcZrUeL96QF/NIOj/b+aa\nOuAKR1i+IftmuST90PNo7QUIPnUA8ekcfO634asvw8g8avT8tU3mk6qA/nAnRO8gnn+bofEgTW45\nCehKXUefV6tNHPY777CnOMn1g0e4fPAooGLnlVnOIEiRMFlC5AlSoJkkfUzSOz9LT9siASfFq2/C\nhUuw77Dg5BGDnTfmMeaveBsCaC6rf273HlphWYlO5jnOIt0ME/Cm0Pp16L13JJCXMOTAnJuh132B\nT7NElGmiTGPhYgmXgOlgWU7JGaMhZQDbiVBwI2TdCNmFAIVnC8hLKaJvXS9ZsgBSk61Mnd1DYlTi\nFEYpe+gb18UahRbe00vw5iCEp1Djhk6P0IhE0QO7X+vWv/UMMAcyD7IAxQLEHZV2OUuV/SwKdKOY\nb97PfO89aONUCDXPamLl2Jk0asG7fv/aMf4Dg0+FdqkKOKoTourwn9+qtmkRWqp+ChgHZvFCpLeq\n/2hVzxOKJ1x43KZwzSFxCoyFssl11fgX7/+iLZk7laFQlCwZVwky5z3F9uippJKNjcShiINNngIp\nFrMZJkcLBMZgekpVK3sixOLHQ2T+RiCH02BXZ6rcJM17hRWWvwH8Y8pOhV+XUn6nrieuCVXxTuY5\n7s4Qk0MskCkxLVT68/PAiAMBsuzkRe7hRTpRTsyggJAB4QCEQ76QXA+2o3Y8SduwIGF6EYa/o/jL\npTIEPz3VzNS5XRRGk1AwUYvBrlO1cm7L6GIAs0mYzJQnputHmTmW36V8Zx3fIEwVXRFQ/i2QHv0j\nqBlAUoLhAH8BXESZor6o7rGlvFI/BGCZJiFD0O5AtysxECUbMpS7zpyAvCEoIsF1QMoNDEk1V502\nRhOf8VoLmvpjEhS0kK7SSSvirNaLlWijn1WKb0qjOtd3/yVM/j1q9P8lVY9N4xO/H0spFMZdNubn\ns9jfLZK44BJaqJzFr/ZupaH82POv55k5XQSRwBRDpYbr5SRa99EvJgnMC4mFS9SRBF1lTJQxQfqu\nMLOfaiU9ZCK/maa8MAwaSbdRj+b9ZeD3gf9Udf53pJS/U9dT6oLfawvtyTiHJ4YwZifIFPKlhFRQ\n2TStUWk5skNAiwUtJlgCTAGGoZRD21WfjvSMHS4kXVhyYE7qvIGVKIXaL4AYRLGWA3Av8BjK9l2B\nTaZL2dJh3N1J8LE+Eh09zNJNgSBBChi4DQlzv23S9JxLFjYBCgQpEqBAlAzNJGmxkzQlM0SXcpi3\nQY7B1AJML0AkDWIGSApwLdRqwvtQy58rsOk0WbuNldHvubYWJh67l8SRI1inkgReSyGIIkoLaFwk\nLhKHzP4mkifb6UqOcPjtUzTPjJFGcWfjQvwe4APA16v/eFdpoumh+RmWB42uB6tN8rVeXRKQuuCe\nT0DyCCx9qfqSLaHJgcBNjkTn6Qudo8lIlwasatOSPq9nC2YE+h+EvhMmpy4+xukLj+PEDESz1x5H\nKTVSL6AMAU0gWiHWmmJX6yj9hdvsffMWPZenuJmFUSyucIzr3M8Vwtil1Xj+NQabZ/M+JYTYW+Ov\nTTaTaeGtXnV7aolD48MUZue5VZD498SprkQANYZ3CNhhQCQAkVC5go7jadm20rSLrno5SdQMbhYl\nk+MsJ5t+L9YCoIW3DWo1YZIarLvp5kPpPTJ4opPQLx6lcOBOxjlKhihRMljYJZfRWiiHdSmhrcO4\nwmSJkSFAmgApmllgF+PszE3RN+HQdSsHr4L9BlySaoP4SAZFvKThCe87UERahq0wqa4Kv0ZZBPJt\nzYw88wTXPvpREoVJUq9No+ZnHZTFTAEoIPf3IT++j/smX+bYxDDtM2PamrQO9FPK67C8iu8qtPDW\nnb6xkMeVsZbxo8Iu7wJt94K4VavoltDkQOAmPxqdRQQHWRAqulsbVapNSHqFg0SFwu97DE7+jMWp\nv3ic0+O/SrE7oDbLlSh20ekGm1ALsHuAfujdM4W15zT7Uq/S///kuGNiipSEIdvkOkcZ4mNMcBub\nUcpvprG3sRGb9xeFED8DvAn8s/UnkfFPAB10jgkxFsN8EYxpifAlpPKvHLcB0WoReqAN82Arb56J\n8dLZKFbRJCD1KjJHRb+54DgGttRRqxY5oqTpIGXFSEVNwl1x7j9+hgM9l5h4E6bPlzOCV0wtV6fx\nJtFlOSZv7mLmb55gur2bqUKRgjNP0Iu9lXUav6ujBUwvfjeASxCDEEGCNBFD0IpFa7GdlsQeYgtZ\nGAb3lslkfBdT7OLtXug7Bq8Od5FKBsHOUXt+tHU0WQnVGqGbMkm/0cJisYfc1TkKZFBvtXp1nIRR\nF57LkuhKcvuHjyGP2hTeGIZbC1UxURtC/TTxPbCW3boeaAWgOgpnM4W38P2GGhExIdRYqYVeqVQJ\nG+QTvxs3h44R6piIc+TMIMmbU6RyhdKa2lrt99M2J0Kcid7Nxfa7OGf0UkxcwLEDkPZ2knBsf6Sx\nmv43GdBukmhPMtiexo60M9P2IfZ84h5yp95CXrhJ4kI743+xl8T5JK49Rpn/Gtsza73C+z8A/6uU\nUgoh/i3wO8DPr+9W/rAel9IbHo1BylRG7XilrU9nD5AAbQFCz/QgntnLq/++hxfe6kYUQ2AH0ZqU\nMk6BikwNIr2lgpIeXA7iWr3I1iADh0Z5/BM5Tp64jP37kpkq4V1Hz9lEuizHxJXdvLH4BPOOg514\nE1mYR2B6tmkHWcfLr6569Wo5db8wghAmrSpEzHURjvRCL4LYzkM4fABzp8C6D3JyiMzNG5BN+e5c\n0sW2lCarQQsUCbhJk+xLTSQvtCEnBUobzqDS9ujEY97+XEMzMOGQ+mEY+afHcTIRWmaThG8tLLMZ\nrxON0cT3sPU+W/eurdhSbzWbd4WAjKAmO1nKtpuyu3QT+ETLEh0+pX53jMc5eHqEqWsJRrJOaWu4\ntQavrIhwIfQQg02fYtoZw1k8DfNBMHQ+9BwIHY6oH2eBGSZtSG6YSUb2tvHaFx6n7elmTs79Xxw+\nO0TybAtT8T24Q7eRBRWvsnI488pYl/CWUs76fv4R8I3Vr3jR933AOzT84zNgtoG1n3jxBjfnAwgb\ncl4gg5/5NOFzMso1+24WCg9z1ckwTw4IgQxSzoSnCaL1dp3hrAgsKiN4xmR6KsXpN+5ETueIDF/i\nEDdIo/wsxW6QR73bjAH5EdTKuVSpfY3RxU+T+lZYFosB0ukY2XweFlwvhWQQn5VuzXuUsRbr+jVo\nv+XSRFHkNkyhsj3enoFCArgMXMC/mrAxmmzuCssKOCAzBq5hevYPzU227/BQdKFYJDkYZPh7EaLh\nMHe0mPTdAdPTMJdAZYGtyzw5QvUKy0b7T/wbf8yF2zdKKywPeOfXerRfE+5pgb42sHah8kIZUKGC\n+qW7f28B1/fbf8iqzxrnhVSr72UR3CLEm7ythm+/AckXUH3nuXXRZO1V2xK1MU8vmYkRZs+4xGcc\nbF98t78H+HV2iadM5w1S5zu5/V8HyF5aBDvrhfUJVt6dS6KMk5ICLoWwSeZWK6Knm658lJNNkhvJ\nBPa1MVhKqMVwJaLd9I76sK4VlkKIPimlTpj9k8A7q1/+5Bq39gXUWx0QPsy8fYEr2SBRRzkWdROr\n+SpTjHFu7n4u3foomfg5lPDww8/i2q5ZRPXgHLCoQnXiLovZEN+ZPMbl0FE+Ff8znuEG11AyKt+H\nymSZ9h6R3ocycN1EbSb8YoN00TTRc4k64N/rVOgTOnfsamkAasHf+1b6X1PcvxmUgxJIUzAkFHFy\nBcgXUDkNIqgVlk8CLzVIk61bYVnWjPBzMpXbUUG53QbJQcHQf5Ls2OkycBCOnIQzb8KNuCe861rU\nOkD1CstG+0/rT/08d89Pcej2YN3Dsz/GWgI7O+CBAxB9BHgCxXI6HtI/funJqn8NgP+cZjM/W9i+\n8rWONJCE8WaI2EDXgxAbh/Ql1ArL5zdZpoB6tzuBkyxOXmRoIaD8XfnlnO93buv83lHAyBm4r0aJ\nj3Ygb0W8d+2PZqmlAGn7tddJswJ5zcJ0g+xbMnm41eW19CwsXgN71mMkXaMBlNKi8dKqLVzvCsun\nhBAnvSeOAP90rfus8gQqetSeINwRJTsVYv6qwE6X89lBpX0tAARyJpnBFuaCPTAe85VaTbPU/+nt\nnwTYEtu2mcvkKVhNvNVzksi+ALeCcCsEk9H9OFfnYTwORYfyCssMaoYHwG9vHl1WqHbFohz/XKTO\n5fElrBXhqh9YK/Y0T2nH+6zwlam5mnBradIoljXXzyu6nYonnQxkMwXGzTZe3/84ue49BO+6zKFO\nyTsTNswWVChhxX009GjxNcp88nv6z8ZoUmUrWYu79SX+BBG5/hYWn2xltC3M5GCAQt6AjAFFqWae\njqxcsOUfq/Xv6lW5/gXMfo3dFeCIcjmPTy5OtJAWt+HlfwtzZ1FS/bfWR5NVW+1RZyACeztITUSZ\nHDEwimXZ4R+q/dZQB7AM6I1CJOzyRmIJJzEG6QQqX5I/8UCtqVfVjDUHDEPeDnO14x6ef/rjDJ3b\nBW9PoEZPXZNG+q7CeldYfrnhJ62IKiviYeBjYJ+F9BiE0uVSpTp5pUNALAuBK6ioh1FdYiXCVqte\n/t/adDNJJhjm5UP3cfGenyDXBtlWSL9ZwPnWBMzNQdYBPkllwNG/Rkr5+U0gyMrQATml2Zp2zAhq\nT+FWQz22NT8j+sv717salJdr1lxNuLU0qRfV8hlYbifQ8HtV8oy6PXw19yjXw7N85sE/5v7cCN9/\nrgDjOZD+1J3+m2tB8lkqee5XG6dJDT+Lf65QC9pAqGszc6CTCx85zMXX2/n+V5uITwXBMZVAko7S\nAKvtCasdrHbeUEepogY4gsXpdhaGr4L9CTA/BLa2S29G39GzKC+ORLhwpwEfCZB52WRmGmJVwrvW\nUAsQtGBPO/S12nTPj8P8BXBmvCtsVJ+rpdSAPz8TOKroLUmqEOW5n/wgbz51jJnCCLx9i8qY7vpt\n3RrvgRWWlQK1pXuJtruG6ZibgVARl3L4up9cFmoyGi66RKdTEJ+HpJ5qr2YSqD6nY1jKaoIt80zn\nHKbjkbJmMRGHkUkoLFIeGFZKa7NFWKZ5a2aCxjXvzajMWqaX9xhqVrOWHuvnNId0VjA0GUIE2zhc\n3MOCW2A+mwM5AsR91+j7iRr33gCNaiyBXOtu1b51s8UhuCfH0hmLKze7mLsdoyze63WW1dsGvyHC\n/1tCfIHlZqrNgi9SW0i6ds7Sdf9lukamsC275JLWb7f6Ki3URURgHTUJHQxgvp6GyQmUFPYbV1Ya\nPl1fOQluAdLT2LMRJiYkE6MRiAvWG3jqRz1mk92oBTq9Xs3+SEr57zdnz7nlhsi+2ATHejL0tg4S\ntnIV2xT5dUudCha3SFNqDrK3wdaPX0mo6E60POaiokyhADeGYHq2FIDAXNYLhSugkl59HTXtE6gF\nO7x39mvcclRbDF2UEPv/C020b8Tji1QSBi8zNWHz9Ug3USKMzWZQzg+9rMtv0PNpXiVeSeHns4bo\nIuSGw1s6WOAwNpcJEKCXsq9Fv8eVguf8qHegriXY/P6Yal5R2DivlOWIAPa13uT+3aM0tV8haBZK\nsxF/eg1d1r8FgtskyDwUZumpGLmkhLd0FJUW/Wt5qv1tzwGDkJmAl4HLLkyumcu8LtSjedvAL0sp\n3xZCNAFvCSGeA34Otefcbwshfg2159w/b+zxy7WSndY4D4SvEgxcJyOUJu03cujvgQC0NYMVtIkk\nFiAzTuX6yLVs3qv8diQsLMFCwneyekr8oyiHSA74j7qQ3odvAzShcux5Tyq1tSpmoLY67UMJvj/U\nf2wOTd5V+Ad5AYU8FGZJL8JVmlFL3W1g2neNf7osobMJupvplHl6i/fTGYvhGEVOn/8zhBBHaKT/\nGBtngojM0OUWaJZJjGXrCv1BAyuNErWUnlqop4yJ6j99KP/J/944TWo+tyxYBZJ+e5RHc9Mki0PM\nykKFcbSW+qbP5YMhRnfvYfL4ADNd7Sh+1pFrVZFJa8IGFlT65lGhjmU1WN/7rcfmPYWKKUBKmRJC\nXEEFG30M+CGv2FdQsTvr6JR+jwfscsd5uHiZpDPNNZkrLUmuZjEzCk0HINThELoWh5EpNjdCQavc\nGtqua6AyiHdStv12oxbXr5cmVVNIvzPofYMm7wAVvtjFxmjyXoDuVHoq7OfCWnbyICU+OdQHTx7n\nZKzIp+zT7Amfg2b4iEr50lj/EVIdG4AhJabjqJj9Utu0EKo2JKx4F9Z2k9YjvNu9Q+ILb92ATNHP\n9W8ULtk5M829l84zPLHEkm1XzC1WMp4CZIjyNieZ4yFuKm8rZS+CHuAaeR96YNeL76sX5KzlwaiN\nhmzeQogBVMDca0Dvxvec06OlZiKL9qUEB4eHmJxJM1xU43J11wEwohA4DOF+F2spAyN6cbvf3rhe\nVFsMq8/5jyW8sQ3WTRPNTt5g4Bj+NC/vQ2wGTd4rqBbg1QZo4TtvQKAJghZtrWE62rMcj81ywh6h\nPzzCWDnTWWP9R2x8FHcxsKWFzr5dKTAaYbS1hHOj9p0F/WUDMkX3RT1dDYFsInq7QPdr48yOFDGK\nlcEy1fDHGRWLYYamD3L95oPML95AhQNv0G5Vqqd/6dL6nZXQgPD2TCZfA37J08DXsD2seUfKDbFR\nWksAYzCE+azAuA6kKy2Jfuu4jIF7BJzj4F7WpNf/ruQJrhd+J44fflumDv34f4FnUBn11rLHVKPU\nGu8IKi99IeCtDFpn9X+gKKDCKH8UZb5slCbvRfjtwtWONh2Z4gI5aN0DXYc4OneOJ7/xLLvEBQbl\nPNeBf33eu7LR/mNsXPPOixAJM0bWiFKZA8fveN4o/EF49SCH128ap8mKMIAekH2kBs8znTZIzIKb\nL/dqv9isju82AStlkXm9nZmlXeSuaCXEH9jfaLV0pIF/tuMfQLdQeAshLJTg/lMppd6eaJ37NQ54\nh9ZWdIdQeQGLo01kXzYoLIDMlJvnj1sVgBMxSPWHKBxuIt+upzP+PZ83qrbqAaBK0w4FIRJEcAVS\nXwOjA+lM6Mc1SBN9337UurkISBMKITaQxu4HiCHgm6gEFiXN+we2h+XmopaW5NO4vc7YFgvQ3hPi\nrqkRPvDONzmbn+brwDkUVXyomy7xr/4JF4Yb28MSKnXFjIgxY/YSFy04yzZTW0mAVM88NarDbau/\nr/X7Jiqd8lmU+XFS33gDe8B6VBEGRDshcoDkUgfjE4I4y4ddrWX71TFDqNQkZlHgXrKI3w7CpD9a\nfr22TP/gr92j/vyOOnZ8xDvqQ72a95eAy1LK/9N3bh37NfpRJRRFH4hjzCcvcXU0QCanMgDCMvcR\noPIOjJh7KQT6mTO6KBNFC/LNgrZ9e/a0XX1w+CCB4W/SfOI+2o7/CJPfypIZeQkaoslTlDuB49W9\nDWiGQrMKTiisfPV7E+dRmQV/BNW2RmmyhSssNx3afqk1MmXHfdi6ykfC3yYSuMgcSfpQsRXHgY8C\nv1q+Qd10af/cz3BibopDIzdLiR3W0tcqY7hgli4yHGfMSyVcKbqqbbjV9n3/d237DfhooAWRnoXo\n81ZVOf3/w8D/hlpd+Vng4w3TpHKFsn6GVHmgD8WQRztZuBTl5iVB3qnMAuCnkX9YiZhwoAlEuMBr\nmQlIXYX0nFdiY1py+SkGalV0xPe5hDIfDXiHxkur3q2eUMFHgZ8GLgohzqFq/+soAv+X9e05V+1+\nBKJdEDvMYraXmzMmhlu2GvinOfrKDFGGzH0sWUeZM4Lev5qRNss+JSg7otTAEOloI9gyTvz69yiK\nXUyNXCY/X3qhG6CJCaINRA9OsZliCpwCalHX+wKjqI0YelDRN6V3sAGabDI2gy1KN9K8VkQIh2jU\nIBaN8EDsMp/kawzKLK+jtmF4CxWXpNdXCiF+lAboYprlXaBqeWPqQTrXRGqhj3w+THs4TyDqgCyi\nlmfnKS3SQSjTHUblZ+m7XpwVQEqtMGmhHESqBMrIUr8Jet/Vb0GQPBdI8R2EOAzGOaTTOE3K8Guy\nNliCwEGT4AcDZLMGY1ch4KzsCfNr3uEg7O6GaFOR9vEpmL1JOY5/I8K75KkDYRCJCiIxiSAARMhl\nM2TTAtfVZet7Rj3RJt9n5YxHG9hzThPdo+x9Eh5zyb/lsnTKS/Rf9WC/mT9LhGn2M85dzDGPWmq6\nWZJOoG3w5fW9e4A93JcY4UPFqxg/93mWonNcj+3lXNMHGP+Xn0dKuUBDNPHZ6A0Tws3IUDcJO8pE\nBpLF97rVxC9K9qO0KSgz+m+sgyZbhGpFckM3qrxJIGDzxKNv8/STc3Sdv8Jb54sszoGwFVV+k7IR\n7n8BfDvE1EUXK1hEGI3NJrVHRhsP97wzxsCfneZE3OKDx10KewzImWBLL+G99CaXonw4qGXupd8C\nHAPXtZDSxHEMXNdASgNXGkhpIr3thl0M79NEb2OsMlaaLGEyzRdJHO0g++HdvPp7v9AwTSpRfieW\nZbNn9wh7782z+9wohuGW9H9tnKhWBEuDYRvwKNDvwHczMLPkXaHj4dcjvP0zA5tAoMj9T07xgacX\nsISJicmbrzRz+vkWkvGg71lrhyP+gFZY+qdgAgSYJ4qYn8/iWEWSb0lEurz+y636lEBWRhhx9nG9\neJyEexklvP2ToI3WT42KSnDnsYxOAsYx7ku9wc+OfoX5C5OcHofpD/0Y4f/pYxt4jncYBkSbcJs7\nSSxGmEiWt3pY+x7+z3cT/vdYK4HBeiHLt9/MZtWcmPm771rUruJbJEYAIi1FHv7ARX7xC6d4/Q/g\n9WeBlJoUQ3kx9XoRCBYx1iG8/WaCHVcmuX92ktghlGVLohgsR3nz6bzvKFSd95JTSRtcL5W1bYBt\nq0R7rusNULIyPYp28fmXL42j8ixO7ruTpU8/w6ullC/rRfkdmoZNf+8IHzg+hNkziiGcUj43nYPL\nL7wrDEdtYD9oUrzHwB3MwasJyiYinXmrUfjMTKbAihW5+6FxPvNPLmBZEtOSSPMOzr12F8m43g5N\n29dXx7skvLXNy08qnQ3PQVDgSPgKx1om6QqfJyTy1SlmcCmPmhJwMhaZ602kjHYK0yHvvv41VI12\nF92j9RBhowR3O7Cfh3cP80T/aQbSb3BlPkk2oTKkNc2acCa80k3XeJ5Pi7OAneDuhvh1GF+qsbOP\ntuKUBmXdXk3bdysw3D+M+odW3aYOVIbB9aAqjEonum8k220tmKgw9E6UMxhQtAtTTp23GqoNFg7B\nJps9T7gMfECZuE7/Hky/BoGC4kB/xtWNICTymMJpaPJQrSdOZOH1eW/lwqL3h66gP0Og/3OFxFNS\nC2upPqX0Hb7L/DFfkrIFPI6eFZjkWU/fqW5pOTWi5TocnB3lqWujjM3dYky6q+rMfpUjbcS4GtmD\nHetnKqBXoWpbfT2C2/929HWedSHswtG9uIfbmJuc4NpvLjJ+8hHG73mUc3FIO/nVblwT61ke/4dS\nyt9vbMNQ7UjUpNJG+wCQRyA5ErrCx1qmSEZuMityJTmlxyB/KlgAJ22SudZMMt0G02EqCaZt4PV2\nG6t29NYAABRdSURBVP8Y7M9xaaME0TEe2X2af/bwH3HtYpJvXZf857TKE5c/+yLkDmlaNUATTQfv\nZQdQwvuoSv8wNqj6lqwurs17Qp/wO4TercBw/Vb8UT0Jykuew+g8+o3TxDcA6QlQiI2rGSZq2/hO\nQPugSruf6qXwK/FLtchUEi3QZLPvw5L7flbi/Jbk9O+oPhqSqkQe5YpavhC8MbooZUbRpVakeS34\n/UQA41klwJlHGeJrXbBF0LfWm83MAX8OxF+6hnOltBJ3nZsQS/wxtZZjc3BmiA9eOc2pWcmEx061\nFixXOyxTRpQr4cMsRI8xGdAMp2XKWnG71bNQrRgYqGmYC3fvxf2h/6+9M4+N47rv+Gdm9uZ9SKRI\nihRlHbTlW7IkH0ntujnQFrnqtEmAHEXh9I8mBRK0jdsUCQoUjYsCRVvDSVPHRRMk6eXmsJs4id0m\nPmTLoi3RungfEu9DS3K59+7M9I83jzu73F3u8nBFZ77AgsOZt7+Z+e57v3f9jpuYf/oFBr6+yKmP\nH+FU3e8TX+rHNE5RbryTjbrHP2ddKzFhqGzg9kYg7bINwEdD3wqHnh5h4vwCiwl9tcPPJVxGsK6O\ngKcXmFKk/yeZiF9yvlcOpM25IcKKdezDvaeRk8ExTi6+yg3hM7z5epzpKZN0Cn57v8o7H/BwKnGU\nR7/3bbugEjmxj6MAr4nrhgS+k2G4IlI1qYi4wtK3M2tYY0JmPpKrSLcbcixTBVRCXTXUpthfr/FA\n1yQBn863/+sfWMykbiyRE4/1UYA0qkunsm6RurYJKqrKjwdhHyv7AnEO3t6H5+jLjKevMjkI2fXF\nvhpaSJKAu06h/qSPpls1GpYGcT82jHZ6Ek3P/CKy7qpkBw34CkhXcCiRFy8JNMobeRd6AwXxYFu5\nGpXbRvPdW/6VnLwXUH7lNgY//xDPPfjHZXOS2RFTyFhrtWCkAixdGmTcMFnuBVPPttC2w744YQJR\ns4LJ5EGuJu5gTp9FTFHkYDDXCzV3XpEzY5R2/5of/PvwV1RyZLiXm2PPc8voaW52m1ztvYby7wOY\nfbMQtcfgLW0GvVH3eDknLrEOyLGzvXeS40ofmD4ae1c49L1RksNxhuLGagY6yK4YLsTsNxG2lLeK\ntYwge2CpxNMUr1K5sO1HezxwaD+eY13cP/QKnxv6OudmE7wxlCSWBE8a9h93s+vTPpoHY/j+u4Jk\ndI2gEpAxw1K8Bu4Dcbx3h+FUkiRilBKA1bRNOZEEyKgKci+8RagC2qGhFW5s5fhdCT594qfsbxth\nYDzBT3+2WrBETmQKbjGn17Q0NfVBmtsMKqtD63w3G3YlZwC+ihiH7+il7Te8vNyrMbmavzJGafUl\n8wqeehd7frOC/b/mov7xSbRvvoornl7dasqk0V4bNMBCWe3HZylv+YWNbAXkrtZvpfIu1ble3rca\nMS4dx0MksEteLlOnyGmZ7BK8wD6M1F6CF19itB8WU0J5rxnzWJDn5TtEjAADyUP0xu8kmT5LRnnL\nOml/S/uKvl0KZGbBSdC8UNWJv2I3Jwb/h986+yRtWpxWH5zunUe71AspaRctddd63aHARt3jXwPu\no9SEobWH4IAfmj2gKaAokDTQ9DR766bYWzOFbyLC5cEks3MGip6JFg2ZBih77QSg1q/QdUcPrvYa\nRuc1phaOid3zmCrSWCX1tXxLqyY5f6s2ocEkUBWm1T3JnvQMTTNzVC9GmUnOE3qzG99sN6PzIRZC\nkIiBxxSb0sHJA/zkxRO8etlDJPyv9rctI4lqprf2KEnaPVfp8C/Q5Jpd40SgANoNabz3RHFFq9AH\nujAj7eBVrc0BazFyI5A3kqtbHlACBlpVCr8/Sr0SpJ5rNHKNeiOIfzGBbzEBEyMw2cC1RB3zE3U0\nu15nPjTHRLvGm5ez2mCJnMiK6wW1nUDKw9G+MU78/CXSYwOrTaWU4LeyYcq6o67EcZ8ewa8nuN/l\n4rYPeRmqOMBwxQHCZoCw4ScddaNH3JBQMrGIAhDwR2j0zdGcmqF9aoJGYwljKIASMqg6P4a2LNYr\n7bEz8j2fLYd8We3HQ3J12WSjite+7lvKsstGZK8H+zq4/F50+pq8XJ5OyeqGFFwVaervDNJ8ELSz\nISZ6hKuEPQRs7jPmLpt4lQR7XNPEPaPMaMsiMs+aVEKF3kYqlmpgF65GN/47QzQ0R+kaOcuhq4vs\ni50lmArhvbMa7fguwmc8mGeC1iKB3NTJHcEXxmbc40tPGNpwK9xXD3dViPdTFQiZuBMxDh38Bfd1\n9uD9xwjdr4CxAko606dK2CcjUUDZvcytH3iNfQ8YPPfmg0xdPglBRazpRYCwmRmMyi97EEq7FhE3\nqcOALp2qtimOVK5wPNbH0e5e2l8f4vU3X+bCGTdKOsTplMi/YJpirLkLeL3/Rn78nYe43Pc5cP0O\npP8Jyk6iKjf5RAPtVEY4qkRRlam8pV1dKbwfi+AJ7SHxwm3oiyrUKOK9dHNj+5WyBsu15QBQDWpj\nCk9bhJrGBQ6qfRxWFjjCDIfT/dQPLFM/GEI55YKIxuWwxvmgi9RIlJ6Xlvma6eLBL97Ddz7/Qpmc\nWGNWxQvqfiqSNZzseYmPLv4HZ/uj9JA9+ShWve2jKgB1KYHrx2NUnZ3k+LsV9n3KwzNNNTzTfIwp\nowVdbyY2V4kxHcBc0kQd8gO7obJxmhvqz3MsfJUHXuun7Vwf584oDIyCJxRb3c4qNnZPAv8pn63M\n9mNX3huFXR3ITm0rUM7cNnehIZXWGfyzx4SccnVKrrlmdYrW905x6H1zuB9fYKInkzxcLqKut2EZ\nUGPs94xQ6atC1+JcW53NC4sz8Sn09m6EdmgFDuPaU0nth6Y4fKSXD//zj7j38otcSoYYcitE7q1j\n5TN7WXjcj9ETEh7Vq+5Xct62PjbsHl9OwtCOqi+SfEVF71ao6ryRms4b0WPgSiVpWerBP95Hemie\n5YiBmi5uWCAbrxqOofZeIeBW2DuqcMuVeWH6JM2fYuQP/BaxrkeBuAkxnbrxIE2+y/iSgyT7pwld\nXUadg5playPDJmrlUDPJI210j5pcuvh7GGYLGOOUy4lwBZdVpwOv3kTn4gR3TU4wvTLNDNkJl0yg\nZnqUG159lt2xFpIjfoyIlpnZlTp3zYVU3tIUwFLganUaz0ScquoQLco4tco4Lq6S0GcJT0ZRJqMo\n04LLdBwCcThnwveAql2VnD3vLpsTb8sZGioXqCBBbfg8HUs3UTN+kbnQAtG5zAR5I2knTN3EWEyQ\nSiSI9MFSvYandpDWulp8ZgO1Rj3JJT+poBczoor64wUmobY6SFvVCFWxAVL9U6xcWcYYB+9UZmRf\nyJphDJG98gJW/Hn5PGXw0v/kT5gYnMQHdCGSTW0W27g/WRQKIohCH9Dz0ht4b9m/avxTXvt5AahE\n9bqoPNBG28kGDjPK4VdmYXx8deIk60y+95V1SR67FyM0vtJPbTBCQ9zk6K2A1yX2wEiBmc5MM9I5\nn6QX4hXEo2MsR8dIrfjwXw6yJ3wFbfwyofgU0TTE3B56w4c5N3s/A2GdtGlv4cPk303Ojw27x5eT\nMPT9H5ym8hKETodxT/Shvfx9kjropkmlN0zME8ZciDOSNOik+Mqt/CFGZuIc+ME0xvOL7I0N05B4\nJjtsmCVkMh2hVbNyW9qXB2S37DNxudMsxIJE/Dr90RijMUhGM2uEmk3s3NFO+u/q5OJYP67KwyTD\nH0So2GfLTKLaCexbfSNPeoX9s1c5NnSeN4LxVeWdQESB2AvsevMylZMz6IYHI6xl9mmsAchkIkKr\npyLPvbIxmcwpZ19IlS/tMplKhemo9uIljkaCeeKskEBL6GgJUCJARKT07DOFJ+Fh4MCxe3jjw5+F\nf3m+LE46Hv4jjnv/hoduHiP6xCniP/o5yZkFXl0QOY5lWtc0QiHuW/dNhfLcR6ahRuMw2A1XBnSW\nXb00uyZowM1YOEWLtxIzrWZ+bKtTc7nS+LQYuhFlIBrhuWXoSGU/TyHFvQ8R10QGDfgLSXkZvOy9\no4WbZoJUD83iL+Fd10Op5bZapoJQTwcQayT7m2o48Hef4Km7/1RcL6v9HAE+SqC1kc6P93Ho7gsc\n+f4gB7/VzfxCmAXEbzOAyOyQD/YdIxMYn1nhth/0Utc0wv3t0PROxMy2RrGWJU3Q4cxQkuNNHjEA\nlBFjl1W4pnFtzs3IrJfpGZWL311hd63C7LUlonGImmCoLi4N30rP/36E0PAFUunz1t2HEDqhjUxt\neqEon5txj/9YqQlDh+eXeN9tBmp8GuE6q6FjkiYT30w56OG1Pp2TXWJzQOgS0zoWH9X6CyY9fTpt\nXWkgjMKKKGNaHx1US5E/NQT3tZLdS8o5rpxPheGpZehUxb6BXC4HMRGqBuYPNjB3sJH59na6fzjI\nXM9ZFFcTpL+CbfJVRhLVUUS1EjZsStyP2gcudQnliigh+6ARxE/qWw4RWA4VnPL2AscK33BD5Ron\nM//LJSsJ2ZGqwBlEZJM9wMCLr7ByYVgWK5mTrrk3WBi7hL9qETUURTESRBKwmMjaTsZkY0rFQGyH\npIMQC4JJiCrERmgPcAf5lbC9b4sDlxDWI1JmoVHsGIKbCwg7229IecIVvOT2M3tujJsxUBGTAS8Z\nYzQ7JhEqbT2UWm6rZXqAccTznwcar4W4+gnhoVMuJ53tp2hLHkRL1rBrZpSmwSECfWMY/bNZS6zD\nZOdjz4X9txtNGdwyGcJYAlRQpRVpjKzNjNcvwkkVURniiBHWChAFTxIqDKiNw1wcbloQIpZUaKiC\nXVVw4ZrJ+CkDrpjWIFOOutvXYTAbm3GPX8f+MoOFqhYqHoKOB6NEqSCBDwUdxTb5dZGm4YkFDj1c\nk6WspaOtho4LHRUdBZPXnkhwx8Mea7Coo5o6mmGg6QauuInL6hG7vwvveBdiuSRq+8QQhC8DS9A9\nDPe4YGkFFmMizlkQ0Ug7AP3udqY+eScLb9QTHZ+jovMviU0ZmCn5839pg0lUvUALekxluaeO2WGI\nWCGO7ZtMkG0tL88pOdc9OefyWS/LcoWuy3PSM63Qdfl8OlAD/D1CSZ1r+VWeOvEIQ98+URYnR599\nkpejM/RNpGBKXx0ZyTXlYoqyFBhklJ40/pLvodnO5UKuKtlN3aA0e6Z24EuIbZYmRBgmy3a55Paj\nWg7m0tKqClGFc41h/QiXsvVQarmtlintBG4F/gpYfMcNrPz5e/ja3X9bNid33T7CuxefYLlbZeWZ\nKIlfhIlPLTFDxlq69K0/AQMrmGccQkMwJMaa2VrShCtBeGmO7Fm+NRhMpSCWzOwr+K3n0Vywbzfs\n22PQHZyCM2chvCBCE2zoad8iD8uYpxL3IYV6vLgIECWARhrNtu/sIo3vWTd1J/woGKsVVkZKcJHG\nTQoNBRWDymdVmk4I4ywNE8000XQTVxrcEXBHQQlD9fPQ1oXwIVlBLGCHreMlVkMJVLuhxQ0eq5uy\nOl8CCDcdX0s1xrFWopfdxOcMUuEUmT5tM5tJwvXPTHtIzvmIzmXvadt/SmnZWshaQLoTrIdyyhXb\nf7AbSJlkzDj9MdBmy98Saxq7iAEE57J9cqU3wFas00orHntMQCj+rmpOefssoFS4EMnTNoLMDDRj\nTCk7otx7lOKvWGq5rZYpI3dkfIJFu94Imnctc4QlZuNpJoZhHqE/I2yuNaYQy4DRJbLMg+xYQXhA\nF4PL9ldB2GjUBKCl1qRqNowyPYdJlEyNLB+Kuc1h6/IEWH/bwhRh1taFw8laOJzkxy8LLw4n+VGM\nl21X3g4cOHDgYOux1fb6Dhw4cODgLYCjvB04cOBgJ8I0zW37IOLP9CHMLb+wTtkxhLXZOeCM7fyT\nwCxw3nauDvgZ0A/8FKgpUvbLwAQiYd5Z65naEF4ylxCWXH9YQO6NOeU+W0jmdvBSiJNyeHE4eftz\nshXtZxs4qclTdtO8vJWcXO86ZTsVt4qwPO9AbDD3AF1Fyo8AdXnO34eIp2In76+BP7GOvwA8WqTs\nlxFREe0ym4HbreNKi9iuPHIfK1Bujczt4KUQJ+Xw4nDy9udkK9rPNnDyaJGyG+LlreakSNnroq5s\n57LJcWDQNM0rpmmmgH8DiqWcscdhWoVpmi8jnMDteD/wTev4m1gZTAuUlbLtMmdM0+yxjsMIf5S2\nPHLfladcmdHP1qAcXvJyYj1PSbw4nPxScCLvs+H2sw2cfKBA2c3w4ugUG7ZTebciHKokJiieXsUE\nnlMUpVtRlIfXkb3bNM1ZEKQhst4Ww2cURelRFOUbiqLU2C/YIiWeBpoKyc2JqFhU5jooh5dyOIHy\neHE4WYudyglsX/vZNCc5ZTfDy/XCCVwHdeV62rC81zTNO4FfB/5AUZT7yvhuMXvHrwL7TdO8HRGX\nfDXQe26kxDxyzALlCsrcYmyGEyjMi8PJWuxkTmB72s+mOSlQdifUletep2yn8p4k21m/zTqXF6Zp\nTlt/5xGZo44XkT2rKEoTiGA2ZNIm5ZM7b1oLTIhIZXdZ31sTKTGf3EIRFfPJLBEl81ImJ3mfv4Bc\nh5O1cncsJ9a9trz9bJaTQmU3wcv/OydS3vVQV7ZTeXcDBxRF6VAUxQN8BHg6X0FFUQJWT4SiKBXA\nu8mOKJYbg/5p4FPW8SeBHxYqaxEmYY9UtiZSYgG5eSMqFpBZCkripQROoHReHE6yn39NuZ3KiXWf\nrWo/W81J3rKb4MXRKXaYG9gJL/WDMKHpRwSpfaRIuU7EzvE5hPnMI7Zr3wWmELG7rgK/izC/ed6S\n/TOgtkjZbyGCmPUAP0DEB7oXEZpD3lOa+9TnyH1PgXJrZG41L8U4KYcXh5O3Nydb1X62gZPaImU3\nzMtbycn1rlMc93gHDhw42IG4njYsHThw4MBBiXCUtwMHDhzsQDjK24EDBw52IBzl7cCBAwc7EI7y\nduDAgYMdCEd5O3DgwMEOhKO8HThw4GAHwlHeDhw4cLAD8X85ukXMksDjgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1135aef10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0KQSmlZxqdawYTQj3WVbywdmRqFSDzNg6cTHY5z5zsqwnYKcZtLJYVnUxhnx4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\nwGEj/FdUiQMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1139ce290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S5tGPvgt/dLe4EiFVBdwmksek4rCXxi/UsfPlSxx74Vu4RqYZL/DiLjyFE6YEeY+K5ZGp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\nwbpuIeITSD130/T4e+DEaUUuzlcjC8Y11tjBGu2w0gwhD1OZFi6ag3S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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1145c17d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Lfg4DHwVeq8FsASr63/Z8VnuSt5Ty+frm6F58AfhY/fP/iwq6vOXkrR59y7Ro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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115035ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sDs/0Ps+pPjg7OG/p1ZAhzs5aFfthjanxVk597wTdMw4k4yRYWEC/Enyk5P/P\nUyggv0102X4ce9uT/OJv32Boz1H+Ovcpzrvf9lNPk7uGT9wK1GnUO8LcH7tEJDHEmC5HQLkQQA4n\nfXoX/dn7GU+5IDlBZVeBu4Mm8wajpRyW5aI4VHA+RnxrBPhbMMNS2pGc9Wmanp5g35Es1fEY5otg\n9C3c6bHFBrvtIPbVc+1d7dx600+y5wbMaMzvtHT3LbR3sIMNhx9okxsnKa+CuEJFtdcUZKCJkteI\nTdUzPNBGNDqK3LjjLttIa1XMOysXZ1iqFYlcaTZxLGHzvguSdIQQfyaEmBBCXCn67neEEMNCiAuF\n45lN7eUScNVlaH/fOAc/OUC1OYv+EuT7FpbEb7HDcS+4u+q5+sRRbh6pIuG5iXSRR1mf4P4u8PvA\nn8x9czfQZTshMyzP8/vHvjn33VbT5O5zWN4lfOJHhlenkAVWr1CRwqwCHgEeXSMeqmNkoJX4bA5Y\nS02T7abJwliShRmWlZlNpMPSEt4W9y0Xp7KxKEfz/irwR8BflHz/B6Zp/sHGd2klCKSTox5/PMf9\n1y7zqHGN/O1hzs/AWKn/ZRdwH4zlWnjjq49y47pKMjSKVDnWq3EfA04Az5We2Aa63B1o7drDh3/Z\nyRf+3UjpqS2jyd23jtpuPikIkoCADplMfGMAsmOQqSC1weGAtjpQ2iCYR1qlo2stqLbdNFkMa5tA\no8JnEZgoGChFonurUI7N+ydCiI4lTm1xX63liA/oxBeLcv+bAzw6/SrnbulcnIFsKd13A++C8TPN\nnPnqo/RPJjDyceaLx6xnqLezjMZx9yl/WwSZYTm+1KmfWppsP58Uxo0lvEfhRj/YJ5cYLyvAaYe2\nFvB1Fgnv2Uo3G7Cw3TQpvem8ADbXJLx1lG0wwa5no89fFUJcEkL8qRAisGE9WhYyuN7fHOPwh3o5\n9pEbVCshspfyZCZNMuZ8SZhWL5xohHxHJy/teweXauuJGbcxcnfATLDJtu4tpstbAltGk7fQLLFF\nNJHx3b6ARmvHEJ6aKeJKnoRZWS1L0wvcCzyGzHgbYuVtTteGLaKJWXRIrdtWiPRWKkxbUtFxkin8\n1prIVtsab2OwVuH9RWC3aZpHgXFgi5Y6BoHWKPd87CYPfPIaAfs0ycuQmZQRJgagCOjwweNNkO/o\n4od7nuFSfT1J2zVkYkCcTSTsNtHlrsaW0uTuM5ssiS2kSTXQRSCg0d4xgL92kqSWJ0mFOrMX9KOQ\nf1xuPsB2pex9AAAgAElEQVQQVFwEfGVsIU2sFYNU4gRGIUUng1rhSkIlj7OQYzmvfW9Qff5VsKZo\nE9M0p4r+/TLwvZV/cbLocyeVZ0OpSFt3Ff5IhIOXLnNgsJvM0DQ96RJftwocUuEplSm1mWvfPsLw\n6Si5eD8LY1HWi35Ks8QqosumZ1iqyAqBy8zP1sbzmgChIVlhffUYlsqwrIxXThZ97uQfQ9bpuvkE\nWBddbB5wNNDouskDjov4tF40MhWNAgFkFCe9nmZM72GmMkGYykNyrbzSz7bSRN6Rec3bLLgrs2TQ\nK9qKRUPHQbpI8y7OqqxU1vSzGRsQL3CfCiEaTdO0jJsfBq6t/POnyu7QYlhCKAh04Rsb4sD3X2Cv\no5urwxnuUFJYR4XMUY3YzzsZ+9sGbv/JfkIjI+SiY4WWGyW8O5FL0j7k8/24Mro8+xsw8yoMTrLx\nGZYC+WrtLFuvxNqRwqaCYmcjSljWNDbxvl+Y4OKFHM3/9jOM/e6fV8grT63r/nA3mk06WRefAOui\ni9MDvjqaXWc5Ls6SpYdxMhWVYRNAWri5oR5gRjnBWKIewnnQ1yq8O9lWmpRox5bZxEGWxBrNJo4F\nwtsy4FZKn04WTkI/XrH1WjMs3y6EOFroXT/wyxX2sgJogJeqRpPWQ6McsvXj7p8i0Zcglpo3gtgU\nqPVBsEYh2rKHF1oOcc1sYnZwguxMmI0vPLVklth/3jq6LAWLcax/dciZMrim8PgKBhp5lIyOmDWR\nxk8rwHI9LpBlMyy3lCZ3n9lkm/kkoEK7nSotzu7RQabDIUJ6vuIamkndzXBsP0PTR5lM2CA3AlSw\ni8MCbCdNrOndEt5yTNgLNu/VzCal1UpUdBxksM+ZTUCOQSvOe61JgKtjrRmWX92EviwDO+CnoSvB\nEz93g/vUG5h/PsXwHYjp87vBO1TYXQcH9qh8t+Yo3xEfp58EGS4hl2cpNrZE45JZYr+wgTdYA0yk\nlC4s/EwVMrocYxl5WkXHTg4tqSMmTJjRIWf9xrauuy+TYbnNNNlubDOf1AB7waslab49iTkaRc1V\nNg4MIJFzcSe8l+uj95CKjwJ3WHtyznbRpLRolDyUgs3bQQZ1Fc3bLDnUJc0mxVE4lily4wX4XZxh\nWchSUnygNuLWh2kKD1LDLVLxWabz89HaAjBtCvm9PlKPBxnNdXHt+UPErt9Ezw2w0Rvq3rUQdlCD\n4PGidOioe1T8BzJ4/XeYdU4TESZ5NDI40IJZag5MoqbyxLON6OMOiKcgm+JuND7sYI0IAvvBls/j\nPp/CcSeHqGDzBRsyOFfJqORH/My4qyHSh9wysNK0+LsBxQWjjMI3xdEmxtzZ5azWxd8XOyzVBTbv\n4iJVm4NyzCatyASdhkKvvmya5h9u/p5zKmAHrQacnYjROMrfhNHzw8SG01jR2hQ6lXGoDN3TROwD\ne+n/QTPRv3CRHbRjpjejMHoUmWBg1UaRNutt369R8YDzAKKpA/V9GdzvSdLZOEln1RtcD8wwi0ka\nBxEC2PZk6AjcwdlUxZDjMMmrEei9A9kJ1mY+meWNF57nymuz5J23sP2SHbgLaLLtKOUViS2jS0Hz\n5jIys7JCXcYJtAG1aQj0A1kDQklknHYlrr1ibDNNSpyKCmaR2URf1GIlaMuGCm7+JsTljNI88Bum\naR4GHgF+RQhxgE3fc84NNBKodbL3/jDtLaPYBkOkrsaIz+RIIC1LNhVqAtDQbCPUtJvX6h+jb7qG\nzOsh9P4Y5DfD3qQgt/X8HPBPgbPWiXXQxFrSWVuYrQF2OzTUwt52ONwOB1pA+DF7BEwLMCExrDLx\nup3ZHo18VsFRrxB8wEbgsA8t0ACijiW2qCoDCgcffIg/+P59/O8/eZbJ/zaXPbelexPefWuGUl6B\nrRk/UvmxB/J4O2fJihSj/SbTE6BX4P6xO6C2GZpawZME+kyI5pB2uEpLwVrYLpoUo9RhmV0kvMvB\n0maTtUabVIZybN7jyLhLTNOMCyGuI/c2+FngyUKzryFjd/7VxnVNrvfa907y1Cdfpm3mPPa/niAS\nltZry5/rdsDeDqg76OBvbYd4ceA9hCLjGFxkfnuzjZ4FvYUDpE2+lkJF+nXSZJUIkdXgAfaBeUxB\n99hJjvrpf2MPU2/UMnv5GqYxQ/QNk1xIR20JoDQ34+9K0XpsgLhiY7C7heioB/QEmJVmYPjwB2uA\nKRxeG879HWRuD8Gm88lC3H0Oy1JeAbZk/NgBN15fhmDTMAlPhPNZg2wOMhUQSQ2A90EwW8A+QGWb\nxC+L7aKJhaWTdBwLtOfyoKAXYsSziEWa9+bWOKnI5i2E6ASOAqeBhs3Zc65g5nAEwN1MVc04+4MX\nqc9dZEKLEKUoIQfAoaF0BTCOdTCR38P1s3thJATGEGvXDCpBhMLcBmumiYHsaxap0VhPV+HsbW3p\nqeoYowmMqSThVyH8qhuiNjAF6f4c6f4kNGahHVTFoOFYBnuNA8VZK8MGDee6pOB0f5TUlR7r303i\nk7ci5lLCN3H8gBQYfqCeKkee3YE72ByTDOZ1VL0yVcb0KeSPODB2+zAmbBA2Ib2RU+RW0aQYVv/n\nk3RshdJUqzksS6GaBg4ji83IoyzYoUe6MzcTZQtvIYQX+BbwfxQ08NI3uEFv1Aa4oMYNu5zk0zmS\n3wiRnAqTG8ossEOZQMzt5tzhY6QeOc6tl5vhH2ZgMAPm5nl555EFvgk8gzTVLbpZGTc3kBEiCSCE\nLLGZQwZhF0WOlIMkckPY2TSI27LM4igwY0AuVLhXIbcumoH+cWav13PnUiv5sRqSCTfrzXlOJ3S+\n9s9epO3/+TXufOLfwJposnbcfWYTCxavzK1gN5EuAmgE7qGRqxzlEk6GUchVfJO008FIWxOp3e1E\nbd4N3KsbtpYmpZctTdLJYyNXseatmjrOfAaHrqOYNuRKIsdW2LzLEt5CCA0puL9umqa1PdHm7E0o\nPKDU4GnU8B8LUTU2Tu4nUyQnYgvyI1UVXE7I1bi51XSI2/VPMDjpgVfHmM/b3bwYS7mF2veR5p05\nzbt8mnznD2BgAEQcXAaqr45q+wRe9Q6RGT+RWR/zr2c+JnVFpHQYTMLoNKQGId1ddNKiQ1YeySgk\nBcnbKsnzXTDrhmgazBiY5RpFi5eHNkKj4/zLD1+l6kAds5fuVE6TDciwvPvMJv3IZJSrSF6ZwybS\nRYCjGuy7abBd5L7cNeL6CBNUvuFwyu6it66DSONeplUPJNOsf0Xbz9bTpBTzoXwKApuRx25kUczV\nx1kxj6m6gT1jYMsaKIa11bm1oXel6GczMiy/AnSbpvn/FX1n7Tn3nyhrb8IyYa8F5yH27xnkySe/\nRd31s9iuhIhNSLFjhQb6PLBnL6j7bfRMN9J3cjexgSHm6wuvZV/KSnAZ2I90vEAhG6p8mnzsN2D2\nVZidgnuP4z/o5F1NwzzkPcUPnj/Aiy/tQ65CvEhTShnZoUYaMgOQi0BuFqm9W8izUItXAQ3Gk/D6\nAGTGYDoFehjKdvBbph4XUMVw7yDH3l7PE3/yUf46+vOM/95XKqPJBmRY3n3oBC4yzyuft05sHl0E\nMjasHeqqwhyO3GQwMUHYyFcsUqL4uc1RhjnBGA6korLeEMFOtpwmS0L6mBTDjiOn48pm0IzV47yL\nVSmRAzUBakpD6G7kWMiwtqTATjY6w/Ix4OeAq0KIi8j+/2skgb+5cXvOFYz7nhqo20NH+w3eufuH\niJlubjpic5Yxy6rk8KjUHHCjHqonE25i9Ho9DPYgNeLNxiBSa6gH/lvxifJpYq3vHQp02HE9oHD/\nnjE+VHOZqWEnly/Uk0hVk0j5mDetrDIhmWnIjiDNL1kWvt5SjUKR50NJCA0hBXsMaVYpd10sWVl1\n2VHto4z2XUex+7n08F8xa/yd1WiD+eSthsW8UthoYPPoYllN7oNqX4SuiX5SswnUNazkE7qXG4kD\n3IreSzI7zcYI722gyZJQAReK4cSRNXBkcqjFCcpL/KI0hFDJgRoDJaFB3oOMGoiz0IC3ORaAcqJN\nXmN5y/sG7DlneWSdgAO6XPCoQkrJEv7LGPabKYwJfYEbwADG3XX8z91PEtr/KFdeaoPLEzC5VUkD\n7cC/K/nu85imOU25NLFsh/EsXJwiG47SWx3lalWe/Vofv/VLWV46fYCX3thPLm9Dhk5aIVrLjUID\nGYtjCfqV4k4trdnKyjQK/1dir3MDXuoeUGl5ZxttD3+B9oei2LyCbNrJHwX+ZWU0+UeJUl75PKZp\nPl/4Z3PoUiS80ZGRrP1UpAxaSpISUcmdcpPqd6IPJpDJOesNN9kGmiwJWXpD5D2ImA1lFkR2YY7k\nqiMhjUw0nbVDPgAEgOnCyWIH5ub0fptRqBooXKAE0LpsaO9KY7yWYPqvYrhHUnNhgaLQXFEg7K3h\ntbZ3cq3tg+ixMLw5xtprLWw91ISOkTcxEzm4NkXmGvSS5LJX8MSnR/jkx4ZJp1Te6K4nGg+Sz/mR\njLCScDWQwr2cFDqLNXOsbTAKFM2NYqum7qE0Bz4bx2z20EsT7fkBHrSfXf0Sa8byIVhb77Dc3HCw\nNUGAaDAR9+hw3kQ/K/d3rcT3rQB2AbZZhfwZFxmPA4Ytp/qGeSy3GKXacMEsmfeixDWUqBTeULaX\nSQ61aaTwzgWQUT5WmYni+iYbj7tAeBfmuJo6qNvHAx29PFH/I2p8p0GdIcZCW3cgAO27YWaXwvVu\nO/pNFePmNFK1qHQvve3D3rZuxj0zRArmiiyCO7RA9mF8ZweozQ7R4R/nf/21i5w5vYszr3SSTBTs\n1MDm2/SXg0AuDd20PGLS/rYpqh8X4FWpJcR+bpK+pXLqlfuwIgnWfp/SwWaVB1aQkkgvOb8dFCmY\nn1CYfyfFvdiIZA0r9r+c5C0DBPh9MQLN/YhT04zcMJkZB70C4e2zwS4v1HvhQgJICEgWlxkupw6O\nJQJ11p6NWQlWE5RW6WMdqbTYgSDk4xB3yMX/KvPSohScDFLsxOyQr0Jq3nYW8mo5ifaVYy3p8V8y\nTfOPhBC/A3yWeY/wvy5a+lSAAjlq62DfYR7qfJVfbfgvjHvHOa2ac4Fr1gKkqgoO3weJesELF+3o\n1xWIWMJ7q7BsenzZNNnffp2UN0UEHYiSQaWHFsazXurP6rRfHKLzt8Y48etR7F82uXo+QDJRzBib\nX+x9aVjCu47WR8d5+DcnyQS8jIzA6x/6IvpEmEzcgRDSXLl2Pinefbu4oJA1IAy2R+MtFhBWv7Si\n/pRaRSPIKnqLUsEroIu9cJSja+UQ6Pj9MVqb+xGZGYavG8zEK1u8+2xwsApiLqiJAXEBKUt4l1vA\nzHKSZ1mobCybHr9OmWK9j+VQKLkxF4LrQArvBMQc8rHKEN4LRK4lvKOW5h1AXsjiVZXl9fj1jeFy\nuMFKj79UiPU+L4R4sXCuzA1DrVrRpYNNyHPCjmd/Hs8HxzHSMwx/PcfsWRN9trT+FwzZWhgLPMyE\n7wQ3co0yWiOTYZ5IWyHQrPTeRuTb/lLxybJoojf78TwbpKrWIHU6RqY7DbjJ4OaaeQiRhydOTVL7\nh5Psy0/xoV8a5MrlBq6fDhKPWAPZ0mo2+5k1JEP6UWxemh7O0/jwDI1P5sm5nNQpUzTZRmj93HHs\nkQ6uvhzg5W/9SfEFyuQTGwv1muLnKhaOy8fwb404L840MJhPG1tK8xbIPIBmpND4v61UcCibLlaW\n8GqatwAcCDwESNMmhhBEGDFN4lQWvJZr0Ii808VslY/MSROuJaSQq8jEZtGklEdLx8/vrYEmxTKl\nmBdWmqKK35WVROOEnBPiqrxkQXiXW9uEBDKXIgM4BfgEpEXBv2C9s+WEt3X1OQ8DlXDwWtPjW4ru\nWgaspVYp8wnADcKN50COhp8ZQf+rCANf1cmOgp6ZX4yCfPxhrY2zvo9zx/ckaX0GYpMyygKNxUvp\nzcKy6fHWQ62KfHMVno9WETxgEIr1kemOA1Xk8HMN6DGDBH9ymhPnBtn3mRDBz/Xj+gEM3XQSj1ga\nhCUoNvOZCxMsLqARxV5Py1OD3P/rg+BUyTjc1HGLhxov8+PcMf78S8fpedOOnq+jKP69TD6xM6+t\nLTd0ioV36fdbMXWX3sESCMUO4mJ4kdpYQVBIVDh+LEfyalCQTmQ/AUK0EkIwyzBmxaWSsvUaM+/x\nM9PgJ33LhHMJ5P6vlQYFLPVGlkyPX6NMsYS3ZZpZ6SnzLBT2hXeSd0JMk5fLLlQdyhbe1usNFLqS\nK3V7rtQva/VmrSrLI8Fa0+PfAB5Hbhj6vwDngN9cvgJYfulOqRo01yBa2jhcf50nU6/ij10mOpsh\nk5LsqiBfr68Rag+Au8nk4qhJ7GYGRifBHEaGbmyXDXhBejyUSZOp787ivt+gucOG7b0u7I5aEhcE\nqe4oOVTy1HIh04Unk0UzWjB8nfS7fWQUJ4X9y5DPa2ngmwVLeNuQNM6QdWrEq6rYne9nd7qf5HXB\nS1cf4tyP2hkfTJNODiA3OZxDBXxSNKisbNvio0pAQAG/CT4Db00Af90EdUMT2C9mIbRQL14PSgew\nvTZD3f0TzLZOMjtVSyL0IMQUiAmIGIXd1FMlR4757ffmdvqqaPw4Thwh0DGDO5iQG2ksM1krgJ9Z\nqpRpHqwb4sHXhpjqH2LS0CvmEC2n44skcTRNcfDJ28w0OcgQJ1dG0TKBiQCmMzWE03Xot2PQHSok\n+FgeLAtzfqo1yhRYrCdbGrll47YjTSQ+IAAeFapMgi2Cjl03OeTuJhsLMTwIqfC8h8G6woKkHIs+\nhfOxJHSPQWb3AG9/23cIioNc7vExMvAgjGsQ0QvPOMt8MIGloBRzmCW/yndwric9/ovAvzdN0xRC\n/AfkhqH/dOlfL+Os0IDddYgThzhac5JfmPgGvdFpLhsprD3eHUhdoqUVDr0fAvY8L39vFs5NyCxC\nrBjlrdC4S7EoPb5smox/bYqDvji1b/dg/5kAtkMBxv94nFR3GHBh4uMCe7hJEB+78bGfaZIk57z9\n1gveCIfYSrDuowJZTKIk0JikjkdyZ3ln6iTfOfU2vvGVD9DfD7HZIeAPkTT5BlRAEynkBHL4eJCe\n+xrkyqZGHkFgt4A2E1rBf2icjnuGaXxlFMdoGjO0MEByPSjWVk3A2ZSm8cMj5B4fo/9KG4nr90r2\nGxLQa8rxSRgZkREuHLPIKKgY8FfyWhWOH+fbH6Tp6ds0HBjDWahgtxRUdDoYpkMf4sjFHu55sYeL\nt9LM5PWKDYr2VJ6qoSiuzlGOvesafBSiOEniWfW3Almp71akk9nIvejfGYbBNyEZZuFYXZQevw6Z\nUmqqUpiXHr7C0QS0gN8NuwT1j9zi0WdOcjh2isyfjtN3HpJZyX3FdgKl5MogRZcDiCbgYhrqDt/h\nA898nf21jxM5/RlGzjwCZwVE0sAwsuxFBGnvt7Z/Lg5+rtyHs+b0+Eo2DHXVnaPON4nfHqXBW0uj\nq06eUGxQn0SJD3Lw2jkyfeOkr2XJZxZbPtUMeMLQ6Q/xruAp6g6MgDJK2hZnqKqVUVsTs9eriN3y\ngz4JxgqZtRuCxenxldBk6icvkpsUeHapNP7MYXa9+16UpxXMTIDkmwap7ixxVOL4iN5QcH8rQfZS\ninwszcZv6VYG7DZo96J21bKr7jpPzlzH6M7w8vV7OPNKC4N3TGZnLyGTcauRzFoZTe5p/xvICsgp\nNIg26vJdZFJ+Mhk/cfwk8JMvJIGiABmoyU/REB0l2H0NWzy6wIi0EcK7+Fq2WJRg9yWEyGHva6Z2\nsE4q02HQUtY22VG8RHE4ojhdUSa1O4wbg/QnBvC5PIwVFM1K6NJx8VdQ34wxraVoOVhP/cGl6zWp\n6PiYRDMmiV0ZZ/DKLDNDcqvJSmmRjJgMX9BxpmfRm29RFUxgx026DM1bxUTFpKo+xJGmIW76Ta6p\nGZJz1OxnqfT4SmgSPPhjqjKzeLJJGty1NNjr5hc7GQXSgkzOQSbrII6LGG7yBDGowcw4YEbQ1j9I\n1dnzmIlbRIaimAn5c8u4Uux+LsVcoK0BGQPsY0miZ9OIQDdtN09yz1io4B7IIgihMYOPOF4SOO1Z\nnLYMOE1wGHOLyonMFBPJEGmHg5jDy/j1lem85vT4SjYMbXj4KX7tY2d4e9uPUIZDKFM35ImcAr1n\nMHtcTE+H+NZIlsYMGKmFhMsB+WkwLkFbxwQf3/U8tV6T43sVwj4fL3f5eMV3D31/sZfYQCdkL0DW\nKsTUT3l1D8ptZ7VdnB5fCU3y8S4SIx146vI0ew3a/UPwTDPGwRYmvjxJqnsSi23Sr58n35fAiBno\nM8UVXkrRB+wqo/9raOf0wNFqtKd3c1/ry3xq4n/wzZOP8uff+llGxmA29hqyMNw+1kqTb/x6mDNv\nZDmuOWA4TH70KjNTGtMZlRE0RlFJhyETg5GeBC1uD5ozh92ZwRZPoIVjiwRuP2t/+4uE93iE6m+/\nwfRz57hPBMhnbHPmaGdWamIt6DSTJ+jR6XEneGSvxm/3R7h/t+C3fs7k8K/L61VCl88eukL1oMp4\nd57EjRHSzy39/gcTGWo8ghBZYvEsfXFIpSGnL+SYcmgSDcO116H3jTjNgVso2gAuFBzLOE0HEhk6\nPLIcg2WsOP5+BweedfFd4wCD5n0kcSC3T+tkqfT4Smhy9HP7+Iy/h2PDgzAbgsgNueAJAdMCcwYi\nMYVXsjmqcTGMShKNLDbMqAIZcEymUS/EmNGTiFB2rmerTXTWqCiWUZEe6P66QVbrpy35N9QkPYzE\nE7TgRiGHkxwd6LQJg6DToMZnSB0naEItnMllOX7QDj642baH03vv5z8/9tqK/VhPevynyt0wtD7y\nPF2xKP6+ELlx0K2t73LACJiDkAjB6RC8nYUGECvvLxKDgV6Ip9PQPM652/CwHxRDR6lSUbqqoNpZ\nUNkrZdVK2oEU3Eumx5e9iaqR6yUx3ME0MP6jHG5HluDeKWoOx0kdGyL18DBZj42M20bvzUt0HFAw\nEBgrODQGb1yh/YBjyXOVthOYDN24xO59Amcmg13TYF8eW2Ac73CYSzcbuHq6hu4bPlKZBFJwX1oX\nTZSxKc7dgIdbgYRcbdl1qZj4kcls2aw83gTujy2MN1jKXNLP+t7+ghiJdA7n8DRTwBGmFqwMrWA+\nH7K/dh3Ox8A1Bt/vg32z8NHfSwFzqeBlj5/+U8Psj0Ly+soR1mPAoUJ/Ukhf2lJGteWetRj5LCTC\n0I1B82QM51yxt6UxDhwpfA7aIeiEQLKOrNtP1qliKnnk2qQf+RRLpseXTZNs/3X2PRXj0HRISjE3\n0tJWLR/cTMg6a397E97dBW0apBXICdAVMBSBqQiMAgcJTM7cgLcdWCqoYiEu3NB5aol20tKfoYkR\nhGnwnRsmj+0B1QCHDk06NOoQcEDACcLLnHXwmxfgF4OAHdSaAJGuqUX3LcV60uPLjr88Yl7G80KO\nNwdltdKEteo3kebAhLQ1WaueYtejpXlPJCExCvYw0AMDcXg1DLF2lZ4uL+GuKlL5FGT6wYiw/kXz\nSgiwTHr8L5R/DQNQSYRUrn8PIndSPPnpXh55f4jgfSMEcyNEWn1EWrx88ytT/Mxnb5BDI4cNcxnt\n5+++PMP7P9u36p3Laadg8IMvT/OJT79J7XSEqtkEZM+SCQV48cI+/uP5ZxmZUsnmJpFvyM96afLq\nt2FgslAYMgNmBvK5+aIAxYUBLGeSZZu00i4203VrCU4r2rk4EM6y5I4js6VtCWkOT4zDf21AquV2\n+GeTWKng5ccv9wPZeSvucrUqLPfuUv2qFFb8gxVntFplastLAdDpgYNBeLN+Fy/WPcElr4eUUhwh\ntGx6fNk08eTjuCZTcsbyMO/TbprvuNsGrr+Hrk9CixsMGxga5O2CnE0hr6rkhYopBAo6Z75s8tBn\nF4pEuUlaUSw6xpLtpGIlqaSio+oG576U47Gfk/VPtCw4k+BIgc3yYVsBSkahzxlgBjzBJC3Z0VVp\nsCUZljVMofVD+LJc1SxXMXqpPCxLc0jlIRVnLgM+BoxEIG4KZqJ20jjIGxnQU5SXHr7dkJ7lfFph\n+o6GmgHbeyO0q8N01I3Qvn+U0J4qprqqOPlCksMnpgv7ddiXFd5nfpDh4InVKwKW005B59wPUtzz\nUJjmySlqxyIwCPFpJ8+PdHLm2l5MppBG340RmSM9hfdaZklxS3CrbF2ckbXusd5AcRgrSDNnEiA/\nz6PrxixW4uRcFMRSsCaVYlPPWuPei9OirAlrJRSvCAI2aHLDNXcV/e4OJu2QF1alz42B3cyhJfNS\nHjiY9yA6kdHHHrB5wFYPVfcCXjAdspM5B2QdgpwqyClSeKuA9wd5Gk/Mjy0pe5QF400g8JS0AzBQ\n0OeEt4mmm/j+//bOGNZqMorjv4OGQUkeOIBBEqJhgI1oYIFVMCwwuoGDk+IIbKwwkzDJAIOzEhaR\nODkYSeAFByUQAkajhFWjkZDD8H3l9t1+X1972z7ax/+XNK/pPfe0+aXfuffdfj29Bts/gNf+hw3/\nMbtuXUyXL09KKr6B/AOv//uUTc/q/9MBMPdhT/tEg/V1i7s3GityUkVO0rwqXuQkTZ2XwYu3EEKI\n/lnwMeVCCCFeJireQggxRdx9sIVwm92vhMfinl4l9iFhDt5t4KfS9kuEDvB3Stu2ANeBu8C3wFJN\n7FnCLU634vIRsAP4njDj7Gfgi0zePXNxJ3M5h/CSc9LGi5ysfyd9jJ8BnCwlYjt7WUsnY68pQxbu\nDcB9YCfhQvQysLsm/gGwJbH9IKGfSlneeeBUXD8NnKuJPUvoiljO+TawN65vimJ3J/JeyMRVcg7h\nJeekjRc5Wf9O+hg/Azg5VxO7kJe1dlITO4pzZcifTfYD99z9kbs/JTS6OFoTn2zG6+4/EKbOljkK\nXI7rl4FjNbFF7nLOv9x9Oa7/DfxC+OScz/thIq5l97MKbbxkGxQ39SInr4STYj8Lj58BnBzLxHbx\noqwwlY0AAAH5SURBVJpSYsji/Q4rW8v9zuwgUzjwnZndNLNPV8m91d0fw4uWtelmDzM+N7NlM/vS\nzJbKL5Q6Jf4IbMvlneuoWJtzFdp4aeME2nmRkypTdQLDjZ/OTuZiu3gZixMYwbkypguWB9z9feAI\n8JmZHWzx3rr5jheB99x9L+EGuBeN3uc7JSbyeCYum7NnujiBvBc5qTJlJzDM+OnsJBM7hXNl9DVl\nyOL9B+E+2IIdcVsSd/8z/n1CeEbS/prcj81sG4RmNswem5TK+8TjD0yETmX74vsqnRJTeXMdFVM5\nG9LYS0snyePP5JWTat7JOon76n38dHWSi+3g5aU7KfKN4VwZsnjfBHaZ2U4z2wh8DFxNBZrZG/GT\nCDN7EzjEyo5i8x3KrwIn4vpx4JtcbBRWUO5UVumUmMmb7KiYydmERl4aOIHmXuRk5fFX4qbqJO6n\nr/HTt5NkbAcvqillfIEr4U0XwhSau8A94ExN3LuEK8e3CdNnzpRe+4rZU+J+Az4hTL+5EXNfBzbX\nxF4B7sT8XxMepHyA0Emg2Gcx3eetubyHM3GVnH17qXPSxoucrG8nfY2fAZxsrold2MtaOhl7TdHt\n8UIIMUHGdMFSCCFEQ1S8hRBigqh4CyHEBFHxFkKICaLiLYQQE0TFWwghJoiKtxBCTBAVbyGEmCDP\nASZP9qA4A0rJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11538ced0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PKC9Q7NgF0bSlEg4dJBVTGH2/BG3cjGQQwlto2svBvQvKfg48bwaRrlyGmSqKXpzM4stX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\nXt5pTu7S9l0xVu6n2uQIMKIoyqSiKBnge8DTd2mvD2NbhqIoZ1FTAOv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11580e750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib_pyplot_samples(train_datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cYznx5jUwzoO"
   },
   "source": [
    "---\n",
    "Problem 3\n",
    "---------\n",
    "Another check: we expect the data to be balanced across classes. Verify that.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/A.pickle 52909\n",
      "notMNIST_large/B.pickle 52911\n",
      "notMNIST_large/C.pickle 52912\n",
      "notMNIST_large/D.pickle 52911\n",
      "notMNIST_large/E.pickle 52912\n",
      "notMNIST_large/F.pickle 52912\n",
      "notMNIST_large/G.pickle 52912\n",
      "notMNIST_large/H.pickle 52912\n",
      "notMNIST_large/I.pickle 52912\n",
      "notMNIST_large/J.pickle 52911\n",
      "notMNIST_small/A.pickle 1872\n",
      "notMNIST_small/B.pickle 1873\n",
      "notMNIST_small/C.pickle 1873\n",
      "notMNIST_small/D.pickle 1873\n",
      "notMNIST_small/E.pickle 1873\n",
      "notMNIST_small/F.pickle 1872\n",
      "notMNIST_small/G.pickle 1872\n",
      "notMNIST_small/H.pickle 1872\n",
      "notMNIST_small/I.pickle 1872\n",
      "notMNIST_small/J.pickle 1872\n"
     ]
    }
   ],
   "source": [
    "def balance_check(pkls):    \n",
    "    for pkl in pkls:\n",
    "        with open(pkl, 'r') as f:\n",
    "            dataset = pickle.load(f)\n",
    "        print (pkl, len(dataset))\n",
    "\n",
    "balance_check(train_datasets)\n",
    "balance_check(test_datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LA7M7K22ynCt"
   },
   "source": [
    "Merge and prune the training data as needed. Depending on your computer setup, you might not be able to fit it all in memory, and you can tune `train_size` as needed. The labels will be stored into a separate array of integers 0 through 9.\n",
    "\n",
    "Also create a validation dataset for hyperparameter tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 411281,
     "status": "ok",
     "timestamp": 1444485897869,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "s3mWgZLpyuzq",
    "outputId": "8af66da6-902d-4719-bedc-7c9fb7ae7948"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: (200000, 28, 28) (200000,)\n",
      "Validation: (10000, 28, 28) (10000,)\n",
      "Testing: (10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "def make_arrays(nb_rows, img_size):\n",
    "    if nb_rows:\n",
    "        dataset = np.ndarray((nb_rows, img_size, img_size), dtype=np.float32)\n",
    "        labels = np.ndarray(nb_rows, dtype=np.int32)\n",
    "    else:\n",
    "        dataset, labels = None, None\n",
    "    return dataset, labels\n",
    "\n",
    "def merge_datasets(pickle_files, train_size, valid_size=0):\n",
    "    num_classes = len(pickle_files)\n",
    "    valid_dataset, valid_labels = make_arrays(valid_size, image_size)\n",
    "    train_dataset, train_labels = make_arrays(train_size, image_size)\n",
    "    vsize_per_class = valid_size // num_classes\n",
    "    tsize_per_class = train_size // num_classes\n",
    "    \n",
    "    start_v, start_t = 0, 0\n",
    "    end_v, end_t = vsize_per_class, tsize_per_class\n",
    "    end_l = vsize_per_class+tsize_per_class\n",
    "    for label, pickle_file in enumerate(pickle_files):       \n",
    "        try:\n",
    "            with open(pickle_file, 'rb') as f:\n",
    "                letter_set = pickle.load(f)\n",
    "                # let's shuffle the letters to have random validation and training set\n",
    "                np.random.shuffle(letter_set)\n",
    "            if valid_dataset is not None:\n",
    "                valid_letter = letter_set[:vsize_per_class, :, :]\n",
    "                valid_dataset[start_v:end_v, :, :] = valid_letter\n",
    "                valid_labels[start_v:end_v] = label\n",
    "                start_v += vsize_per_class\n",
    "                end_v += vsize_per_class\n",
    "                    \n",
    "            train_letter = letter_set[vsize_per_class:end_l, :, :]\n",
    "            train_dataset[start_t:end_t, :, :] = train_letter\n",
    "            train_labels[start_t:end_t] = label\n",
    "            start_t += tsize_per_class\n",
    "            end_t += tsize_per_class\n",
    "        except Exception as e:\n",
    "            print('Unable to process data from', pickle_file, ':', e)\n",
    "            raise\n",
    "    \n",
    "    return valid_dataset, valid_labels, train_dataset, train_labels\n",
    "            \n",
    "            \n",
    "train_size = 200000\n",
    "valid_size = 10000\n",
    "test_size = 10000\n",
    "\n",
    "valid_dataset, valid_labels, train_dataset, train_labels = merge_datasets(\n",
    "  train_datasets, train_size, valid_size)\n",
    "_, _, test_dataset, test_labels = merge_datasets(test_datasets, test_size)\n",
    "\n",
    "print('Training:', train_dataset.shape, train_labels.shape)\n",
    "print('Validation:', valid_dataset.shape, valid_labels.shape)\n",
    "print('Testing:', test_dataset.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GPTCnjIcyuKN"
   },
   "source": [
    "Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "6WZ2l2tN2zOL"
   },
   "outputs": [],
   "source": [
    "def randomize(dataset, labels):\n",
    "    permutation = np.random.permutation(labels.shape[0])\n",
    "    shuffled_dataset = dataset[permutation,:,:]\n",
    "    shuffled_labels = labels[permutation]\n",
    "    return shuffled_dataset, shuffled_labels\n",
    "train_dataset, train_labels = randomize(train_dataset, train_labels)\n",
    "test_dataset, test_labels = randomize(test_dataset, test_labels)\n",
    "valid_dataset, valid_labels = randomize(valid_dataset, valid_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "puDUTe6t6USl"
   },
   "source": [
    "---\n",
    "Problem 4\n",
    "---------\n",
    "Convince yourself that the data is still good after shuffling!\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be sure that the data are still fine after the merger and the randomization, I will select one item and display the image alongside the label. Note: 0 = A, 1 = B, 2 = C, 3 = D, 4 = E, 5 = F, 6 = G, 7 = H, 8 = I, 9 = J. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pretty_labels = {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 7: 'H', 8: 'I', 9: 'J'}\n",
    "\n",
    "def disp_sample_dataset(dataset, labels):\n",
    "    items = random.sample(range(len(labels)), 8)\n",
    "    for i, item in enumerate(items):\n",
    "        plt.subplot(2, 4, i+1)\n",
    "        plt.axis('off')\n",
    "        plt.title(pretty_labels[labels[item]])\n",
    "        plt.imshow(dataset[item])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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S2nqU1mWPdtknnqzQIcc16SPXgRx4Mtig2ISckgWMTsHMG+Bmhvhy+Wv8cvkPuGuPs0oU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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116409790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_sample_dataset(train_dataset, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ks8HILyAeVwn1RfCNhanunmFxbZqf/mSGLgd4BZWXPA8TXr+BdEztbOdPlg/p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ICy/PZkK224xycSJlMzNZbPbtOfqdGLW8fewpuh0DwTvnclKOx0FsQs9HegtgNqN+CcqE4Qfmd1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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d143290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_sample_dataset(valid_dataset, valid_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/3oW6R6QdbHGDr2sG+o/0IF6fRjmWQv78XdyfLyPXF7fyhgL5w6MVhhseWhkN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PLbeSDDqEra0LdOH0FAYzWgvRpgcEiOQ97lK2CweFfUX/4sRC2lRxtrF9x7ULw3aGCJBkoNBXyB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\nGDFijBD+PzmClrDfHlYvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1185b6e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_sample_dataset(test_dataset, test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tIQJaJuwg5Hw"
   },
   "source": [
    "Finally, let's save the data for later reuse:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "QiR_rETzem6C"
   },
   "outputs": [],
   "source": [
    "pickle_file = 'notMNIST.pickle'\n",
    "\n",
    "try:\n",
    "    f = open(pickle_file, 'wb')\n",
    "    save = {\n",
    "    'train_dataset': train_dataset,\n",
    "    'train_labels': train_labels,\n",
    "    'valid_dataset': valid_dataset,\n",
    "    'valid_labels': valid_labels,\n",
    "    'test_dataset': test_dataset,\n",
    "    'test_labels': test_labels,\n",
    "    }\n",
    "    pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
    "    f.close()\n",
    "except Exception as e:\n",
    "    print('Unable to save data to', pickle_file, ':', e)\n",
    "    raise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 413065,
     "status": "ok",
     "timestamp": 1444485899688,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "hQbLjrW_iT39",
    "outputId": "b440efc6-5ee1-4cbc-d02d-93db44ebd956"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compressed pickle size: 690800441\n"
     ]
    }
   ],
   "source": [
    "statinfo = os.stat(pickle_file)\n",
    "print('Compressed pickle size:', statinfo.st_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gE_cRAQB33lk"
   },
   "source": [
    "---\n",
    "Problem 5\n",
    "---------\n",
    "\n",
    "By construction, this dataset might contain a lot of overlapping samples, including training data that's also contained in the validation and test set! Overlap between training and test can skew the results if you expect to use your model in an environment where there is never an overlap, but are actually ok if you expect to see training samples recur when you use it.\n",
    "Measure how much overlap there is between training, validation and test samples.\n",
    "\n",
    "Optional questions:\n",
    "- What about near duplicates between datasets? (images that are almost identical)\n",
    "- Create a sanitized validation and test set, and compare your accuracy on those in subsequent assignments.\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# I dont think doing number 5 is currently useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\npickle_file = 'notMNIST2.pickle'\\n\\ntry:\\n  f = open(pickle_file, 'wb')\\n  save = {\\n    'train_dataset': train_dataset,\\n    'train_labels': train_labels,\\n    'valid_dataset': valid_dataset,\\n    'valid_labels': valid_labels,\\n    'test_dataset': test_dataset,\\n    'test_labels': test_labels,\\n    'new_valid_dataset': new_valid_dataset,\\n    'new_valid_labels': new_valid_labels,\\n    'new_test_dataset': new_test_dataset,\\n    'new_test_labels': new_test_labels,\\n    }\\n  pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\\n  f.close()\\nexcept Exception as e:\\n  print('Unable to save data to', pickle_file, ':', e)\\n  raise  \\n\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "pickle_file = 'notMNIST2.pickle'\n",
    "\n",
    "try:\n",
    "  f = open(pickle_file, 'wb')\n",
    "  save = {\n",
    "    'train_dataset': train_dataset,\n",
    "    'train_labels': train_labels,\n",
    "    'valid_dataset': valid_dataset,\n",
    "    'valid_labels': valid_labels,\n",
    "    'test_dataset': test_dataset,\n",
    "    'test_labels': test_labels,\n",
    "    'new_valid_dataset': new_valid_dataset,\n",
    "    'new_valid_labels': new_valid_labels,\n",
    "    'new_test_dataset': new_test_dataset,\n",
    "    'new_test_labels': new_test_labels,\n",
    "    }\n",
    "  pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
    "  f.close()\n",
    "except Exception as e:\n",
    "  print('Unable to save data to', pickle_file, ':', e)\n",
    "  raise  \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L8oww1s4JMQx"
   },
   "source": [
    "---\n",
    "Problem 6\n",
    "---------\n",
    "\n",
    "Let's get an idea of what an off-the-shelf classifier can give you on this data. It's always good to check that there is something to learn, and that it's a problem that is not so trivial that a canned solution solves it.\n",
    "\n",
    "Train a simple model on this data using 50, 100, 1000 and 5000 training samples. Hint: you can use the LogisticRegression model from sklearn.linear_model.\n",
    "\n",
    "Optional question: train an off-the-shelf model on all the data!\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regr = LogisticRegression()\n",
    "X_test = test_dataset.reshape(test_dataset.shape[0], 28 * 28)\n",
    "y_test = test_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>Note on the Following Training Sample Code</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code uses score which returns the mean accuracy on the given test data and labels. In multi-label classification (which is what question 6 is doing), the subset accuracy is a harsh metric since each label needs to be correctly predicted. \n",
    "\n",
    "Later on in the jupyter notebook, there will be code showing on which class the model is consistently predicting incorrectly. This is called the confusion matrix. \n",
    "\n",
    "http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>50 Training Samples</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 51.6 ms, sys: 1.47 ms, total: 53 ms\n",
      "Wall time: 55.3 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.627"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 50\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr.fit(X_train, y_train)\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>100 Training Samples</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 120 ms, sys: 1.21 ms, total: 121 ms\n",
      "Wall time: 120 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.74299999999999999"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 100\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr.fit(X_train, y_train)\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<h3 align='Left'>1000 Training Samples</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.01 s, sys: 8.01 ms, total: 2.02 s\n",
      "Wall time: 2.01 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.82679999999999998"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 1000\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr.fit(X_train, y_train)\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>5000 Training Samples plus Predicting Class Labels </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 20.1 s, sys: 65.7 ms, total: 20.2 s\n",
      "Wall time: 20.2 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.85540000000000005"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 5000\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr.fit(X_train, y_train)\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict class labels for the test set\n",
    "predicted = regr.predict(X_test)\n",
    "predicted[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.64654384e-01,   4.21571315e-01,   9.81267908e-05,\n",
       "         5.62562119e-03,   1.31325110e-03,   5.85427878e-03,\n",
       "         3.58721021e-03,   4.83213803e-04,   2.36878287e-01,\n",
       "         1.59934312e-01])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.predict_proba(X_test)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>Confusion Matrix </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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hgVPSHsCngLER8TayGu6UIu9pZtXWzGXlirI1HtUHAdtL2gxsBzyzFe5pZhVV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\nmpnl5MBpZpbT/wAAVcAlSyv7MwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11629e410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized confusion matrix\n",
      "[[ 0.88  0.01  0.01  0.01  0.01  0.01  0.01  0.03  0.01  0.02]\n",
      " [ 0.01  0.85  0.01  0.03  0.02  0.01  0.02  0.01  0.02  0.01]\n",
      " [ 0.    0.01  0.89  0.01  0.02  0.01  0.02  0.    0.01  0.01]\n",
      " [ 0.    0.03  0.01  0.88  0.01  0.01  0.01  0.01  0.02  0.02]\n",
      " [ 0.01  0.02  0.05  0.01  0.77  0.04  0.02  0.01  0.05  0.01]\n",
      " [ 0.01  0.01  0.01  0.01  0.01  0.9   0.01  0.01  0.02  0.02]\n",
      " [ 0.01  0.01  0.04  0.01  0.01  0.02  0.85  0.01  0.01  0.01]\n",
      " [ 0.03  0.02  0.01  0.01  0.03  0.03  0.01  0.83  0.02  0.02]\n",
      " [ 0.02  0.01  0.01  0.01  0.02  0.02  0.02  0.02  0.83  0.05]\n",
      " [ 0.01  0.01  0.02  0.02  0.    0.02  0.01  0.02  0.04  0.87]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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TpZmVi0/DzcxycMvSzCwH91mameVQ0lw5MJLl+oJHSKy+u9gRNiMOP7vQ+lff/vlC6y/6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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116296750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "# Using Matplotlib. The confusion matrix that will result from this will be a heatmap. \n",
    "def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('Actual label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "cm = metrics.confusion_matrix(y_test, predicted)\n",
    "np.set_printoptions(precision=2)\n",
    "print ('Confusion Matrix, without normalization')\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm)\n",
    "plt.show()\n",
    "\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "print('Normalized confusion matrix')\n",
    "print(cm_normalized)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>All Training Samples</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9min, sys: 1.09 s, total: 9min 1s\n",
      "Wall time: 9min 2s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.89590000000000003"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr2 = LogisticRegression(solver='sag')\n",
    "sample_size = len(train_dataset)\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr2.fit(X_train, y_train)\n",
    "regr2.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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f8rYNhueTtuNGkTLRbU8gHYcriPkdHZ9tM/LtPcbOQUZU0dZtWo0YpbUx1j84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\np2vkHY6s3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1161b17d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_labels = regr2.predict(X_test)\n",
    "disp_sample_dataset(test_dataset, pred_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# http://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "default_view": {},
   "name": "1_notmnist.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
